| 1. |
- Jonsson, Pär, et al.
(författare)
-
Constrained randomization and multivariate effect projections improve information extraction and biomarker pattern discovery in metabolomics studies involving dependent samples
- 2015
-
Ingår i: Metabolomics. - : Springer. - 1573-3882 .- 1573-3890. ; 11:6, s. 1667-1678
-
Tidskriftsartikel (refereegranskat)abstract
- Analytical drift is a major source of bias in mass spectrometry based metabolomics confounding interpretation and biomarker detection. So far, standard protocols for sample and data analysis have not been able to fully resolve this. We present a combined approach for minimizing the influence of analytical drift on multivariate comparisons of matched or dependent samples in mass spectrometry based metabolomics studies. The approach is building on a randomization procedure for sample run order, constrained to independent randomizations between and within dependent sample pairs (e.g. pre/post intervention). This is followed by a novel multivariate statistical analysis strategy allowing paired or dependent analyses of individual effects named OPLS-effect projections (OPLS-EP). We show, using simulated data that OPLS-EP gives improved interpretation over existing methods and that constrained randomization of sample run order in combination with an appropriate dependent statistical test increase the accuracy and sensitivity and decrease the false omission rate in biomarker detection. We verify these findings and prove the strength of the suggested approach in a clinical data set consisting of LC/MS data of blood plasma samples from patients before and after radical prostatectomy. Here OPLS-EP compared to traditional (independent) OPLS-discriminant analysis (OPLS-DA) on constrained randomized data gives a less complex model (3 versus 5 components) as well a higher predictive ability (Q2 = 0.80 versus Q2 = 0.55). We explain this by showing that paired statistical analysis detects 37 unique significant metabolites that were masked for the independent test due to bias, including analytical drift and inter-individual variation.
|
|
| 2. |
- Lindgren, Moa, et al.
(författare)
-
Type IV Collagen in Human Colorectal Liver Metastases—Cellular Origin and a Circulating Biomarker
- 2022
-
Ingår i: Cancers. - : MDPI. - 2072-6694. ; 14:14
-
Tidskriftsartikel (refereegranskat)abstract
- Circulating type IV collagen (cCOL IV) is a potential biomarker for patients with colorectal liver metastases (CLM) who present with elevated levels of COL IV in both CLM tissue and circulation. This study aimed to establish the cellular origin of elevated levels of COL IV and analyze circulating COL IV in CLM patients. The cellular source was established through in situ hybridization, immunohistochemical staining, and morphological evaluation. Cellular expression in vitro was assessed by immunofluorescence. Tissue expression of COL IV-degrading matrix metalloproteinases (MMPs)-2, -7, -9, and -13 was studied with immunohistochemical staining. Plasma levels of COL IV in CLM patients and healthy controls were analyzed with ELISA. This study shows that cancer-associated fibroblasts (CAFs) express COL IV in the stroma of CLM and that COL IV is expressed in vitro by fibroblasts but not by tumor cells. MMP-2, -7, -9, and -13 are expressed in CLM tissue, mainly by hepatocytes and immune cells, and circulating COL IV is significantly elevated in CLM patients compared with healthy controls. Our study shows that stromal cells, not tumor cells, produce COL IV in CLM, and that circulating COL IV is elevated in patients with CLM.
|
|
| 3. |
- Björkblom, Benny, et al.
(författare)
-
Distinct metabolic hallmarks of WHO classified adult glioma subtypes
- 2022
-
Ingår i: Neuro-Oncology. - : Oxford University Press. - 1522-8517 .- 1523-5866. ; 24:9, s. 1454-1468
-
Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Gliomas are complex tumors with several genetic aberrations and diverse metabolic programs contributing to their aggressive phenotypes and poor prognoses. This study defines key metabolic features that can be used to differentiate between glioma subtypes, with potential for improved diagnostics and subtype targeted therapy.METHODS: Cross-platform global metabolomic profiling coupled with clinical, genetic, and pathological analysis of glioma tissue from 224 tumors - oligodendroglioma (n=31), astrocytoma (n=31) and glioblastoma (n=162) - were performed. Identified metabolic phenotypes were evaluated in accordance with the WHO classification, IDH-mutation, 1p/19q-codeletion, WHO-grading 2-4, and MGMT promoter methylation.RESULTS: Distinct metabolic phenotypes separate all six analyzed glioma subtypes. IDH-mutated subtypes, expressing 2-hydroxyglutaric acid, were clearly distinguished from IDH-wildtype subtypes. Considerable metabolic heterogeneity outside of the mutated IDH pathway were also evident, with key metabolites being high expression of glycerophosphates, inositols, monosaccharides and sugar alcohols and low levels of sphingosine and lysoglycerophospholipids in IDH-mutants. Among the IDH-mutated subtypes, we observed high levels of amino acids, especially glycine and 2-aminoadipic acid, in grade 4 glioma, and N-acetyl aspartic acid in low-grade astrocytoma and oligodendroglioma. Both IDH-wildtype and mutated oligodendroglioma and glioblastoma were characterized by high levels of acylcarnitines, likely driven by rapid cell growth and hypoxic features. We found elevated levels of 5-HIAA in gliosarcoma and a subtype of oligodendroglioma not yet defined as a specific entity, indicating a previously not described role for the serotonin pathway linked to glioma with bimorphic tissue.CONCLUSION: Key metabolic differences exist across adult glioma subtypes.
|
|
| 4. |
- Björkblom, Benny, et al.
(författare)
-
Metabolic response patterns in brain microdialysis fluids and serum during interstitial cisplatin treatment of high-grade glioma
- 2020
-
Ingår i: British Journal of Cancer. - : Nature Publishing Group. - 0007-0920 .- 1532-1827. ; 122:2, s. 221-232
-
Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: High-grade gliomas are associated with poor prognosis. Tumour heterogeneity and invasiveness create challenges for effective treatment and use of systemically administrated drugs. Furthermore, lack of functional predictive response-assays based on drug efficacy complicates evaluation of early treatment responses.METHODS: We used microdialysis to deliver cisplatin into the tumour and to monitor levels of metabolic compounds present in the tumour and non-malignant brain tissue adjacent to tumour, before and during treatment. In parallel, we collected serum samples and used multivariate statistics to analyse the metabolic effects.RESULTS: We found distinct metabolic patterns in the extracellular fluids from tumour compared to non-malignant brain tissue, including high concentrations of a wide range of amino acids, amino acid derivatives and reduced levels of monosaccharides and purine nucleosides. We found that locoregional cisplatin delivery had a strong metabolic effect at the tumour site, resulting in substantial release of glutamic acid, phosphate, and spermidine and a reduction of cysteine levels. In addition, patients with long-time survival displayed different treatment response patterns in both tumour and serum. Longer survival was associated with low tumour levels of lactic acid, glyceric acid, ketoses, creatinine and cysteine. Patients with longer survival displayed lower serum levels of ketohexoses, fatty acid methyl esters, glycerol-3-phosphate and alpha-tocopherol, while elevated phosphate levels were seen in both tumour and serum during treatment.CONCLUSION: We highlight distinct metabolic patterns associated with high-grade tumour metabolism, and responses to cytotoxic cisplatin treatment.
|
|
| 5. |
- Björkblom, Benny, et al.
(författare)
-
Metabolomic screening of pre-diagnostic serum samples identifies association between alpha- and gamma-tocopherols and glioblastoma risk
- 2016
-
Ingår i: Oncotarget. - : Impact Journals, LLC. - 1949-2553. ; 7:24, s. 37043-37053
-
Tidskriftsartikel (refereegranskat)abstract
- Glioblastoma is associated with poor prognosis with a median survival of one year. High doses of ionizing radiation is the only established exogenous risk factor. To explore new potential biological risk factors for glioblastoma, we investigated alterations in metabolite concentrations in pre-diagnosed serum samples from glioblastoma patients diagnosed up to 22 years after sample collection, and undiseased controls. The study points out a latent biomarker for future glioblastoma consisting of nine metabolites (gamma-tocopherol, alpha-tocopherol, erythritol, erythronic acid, myo-inositol, cystine, 2-keto-L-gluconic acid, hypoxanthine and xanthine) involved in antioxidant metabolism. We detected significantly higher serum concentrations of alpha-tocopherol (p=0.0018) and gamma-tocopherol (p=0.0009) in future glioblastoma cases. Compared to their matched controls, the cases showed a significant average fold increase of alpha- and gamma-tocopherol levels: 1.2 for alpha-T (p=0.018) and 1.6 for gamma-T (p=0.003). These tocopherol levels were associated with a glioblastoma odds ratio of 1.7 (alpha-T, 95% CI: 1.0-3.0) and 2.1 (gamma-T, 95% CI: 1.2-3.8). Our exploratory metabolomics study detected elevated serum levels of a panel of molecules with antioxidant properties as well as oxidative stress generated compounds. Additional studies are necessary to confirm the association between the observed serum metabolite pattern and future glioblastoma development.
|
|
| 6. |
- Björkblom, Benny, et al.
(författare)
-
Pre-diagnostic plasma metabolites linked to future brain tumor development
- 2018
-
Ingår i: Neuro-Oncology. - : Oxford University Press. - 1522-8517 .- 1523-5866. ; 20, s. 288-289
-
Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- BACKGROUND: The Northern Sweden Health and Disease Study is a unique population-based biobank linked to the clinical data registries. The samples originate from over 133 000 individuals living in the northern part of Sweden, and primarily collected during health checkups from the age of 40 years. Our project aims to investigate alterations in metabolite signatures in blood plasma of healthy blood donors that later in life developed a tumor. Brain tumors, especially glioblastoma is associated with poor prognosis. To explore early events of metabolic reprograming linked to future diagnosis, we investigated alterations in metabolite concentrations in plasma collected several years before diagnosis with matched healthy controls.MATERIALS AND METHODS: In total 392 analytical samples (256 repeated timepoint and 136 single timepoint, case-control samples) were analyzed using GCTOFMS. Constrained randomization of run order was utilized to maximize information output and minimize the false discovery rate. By use of reference databases, we could with high confidence quantify and identify 150 plasma metabolites. We detected metabolites with significant alterations in concertation between pre-clinical glioma cases and healthy controls by the effect projection approach based on orthogonal partial least squares (OPLSEP).RESULTS AND CONCLUSIONS: For the repeated blood samples, we designed and applied a novel multivariate strategy for high resolution biomarker pattern discovery. We utilize the fact that we have available samples from two repeated time points prior to diagnosis for each future glioma case and their matched controls to construct a small design of experiment (DoE) of four samples for each match pair. The data for each individual DoE was evaluated by OPLS-EP to determine the effect of each individual metabolite in relation to control-case, time and their interaction. Finally, latent significance calculations by means of OPLS were used to extract and evaluate the correct latent biomarker and highlight true significance of individual metabolites. Our study presents an approach to minimize confounding effects due to systematic noise from sampling, the analytical method, as well as take into account personalized metabolic levels over time, enabling biomarker detection within a smaller sample group. We will present and discuss the latest results and biomarkers from this exploratory metabolomics study at the meeting
|
|
| 7. |
- Borgmästars, Emmy, et al.
(författare)
-
Circulating tissue polypeptide-specific antigen in pre-diagnostic pancreatic cancer samples
- 2021
-
Ingår i: Cancers. - : MDPI. - 2072-6694. ; 13:21
-
Tidskriftsartikel (refereegranskat)abstract
- Early detection of pancreatic ductal adenocarcinoma (PDAC) is challenging, and late diagnosis partly explains the low 5-year survival. Novel and sensitive biomarkers are needed to enable early PDAC detection and improve patient outcomes. Tissue polypeptide specific antigen (TPS) has been studied as a biomarker in PDAC diagnostics, and it has previously been shown to reflect clinical status better than the ‘golden standard’ biomarker carbohydrate antigen 19-9 (CA 19-9) that is most widely used in the clinical setting. In this cross-sectional case-control study using pre-diagnostic plasma samples, we aim to evaluate the potential of TPS as a biomarker for early PDAC detection. Furthermore, in a subset of individuals with multiple samples available at different time points before diagnosis, a longitudinal analysis was used. We assessed plasma TPS levels using enzyme-linked immunosorbent assay (ELISA) in 267 pre-diagnostic PDAC plasma samples taken up to 18.8 years before clinical PDAC diagnosis and in 320 matched healthy controls. TPS levels were also assessed in 25 samples at PDAC diagnosis. Circulating TPS levels were low both in pre-diagnostic samples of future PDAC patients and in healthy controls, whereas TPS levels at PDAC diagnosis were significantly increased (odds ratio 1.03; 95% confidence interval: 1.01–1.05) in a logistic regression model adjusted for age. In conclusion, TPS levels increase late in PDAC progression and hold no potential as a biomarker for early detection.
|
|
| 8. |
- Borgmästars, Emmy, 1990-
(författare)
-
In search of early biomarkers in pancreatic ductal adenocarcinoma using multi-omics and bioinformatics
- 2022
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Background: Pancreatic ductal adenocarcinoma (PDAC) is a very aggressive malignancy with a 5-year survival of 10 %. Surgery is the only curative treatment. Unfortunately, few patients are eligible for surgery due to late detection. Thus, we need ways to detect the disease at an earlier stage and for that good screening biomarkers could be used. Previous studies have analyzed circulating analytes in prospective studies to identify early PDAC signals. One such class is microRNAs (miRNAs). MicroRNAs are non-coding RNAs of around 22 nucleotides that act as post- transcriptional regulators by interaction with messenger RNAs (mRNAs). The function of a miRNA can be elucidated by target prediction, to identify its potential targets, followed by enrichment analysis of the predicted targets. Challenges with this approach includes a lot of false positives being generated and that miRNAs can perform their role in a tissue- or disease-specific manner. Other classes of analytes that have previously been studied in prospective PDAC cohorts are metabolites and proteins. Aims: This thesis has three aims. First, to build a miRNA functional analysis pipeline with correlation support between miRNA and its predicted target genes. Second, to identify potential circulating biomarkers for early detection of PDAC using multi-omics. Third, to identify potential prognostic metabolites in a prospective PDAC cohort.Methods: We used publicly available data from the cancer genome atlas-pancreatic adenocarcinoma (TCGA-PAAD) and pre-diagnostic plasma samples from the Northern Sweden Health and Disease Study. We built a pipeline in R including miRNA, mRNA, and protein expression data from TCGA-PAAD for in silico miRNA functional analysis. Pre- diagnostic plasma samples from future PDAC patients as well as matched healthy controls were analyzed using multi- omics. Tissue polypeptide specific antigen (TPS) was analyzed by enzyme linked immunosorbent assay in 267 future PDAC samples and 320 healthy controls. Metabolomics and clinical biomarkers (carbohydrate antigen (CA) 19-9, carcinoembryonic antigen (CEA), and CA 15-3) were profiled in 100 future PDAC samples and 100 healthy controls using liquid chromatography-mass spectrometry (MS), gas chromatography-MS, and multi-plex technology. Of these, a subset of 39 future PDAC patients and 39 healthy controls were profiled for 2083 microRNAs using targeted sequencing and 644 proteins using proximity extension assays. Circulating levels of multi-omics analytes were analyzed using conditional or unconditional logistic regression. Least absolute shrinkage and selection operator (LASSO) in combination with 500 bootstrap iterations identified the most informative variables. The prognostic value of metabolites was assessed using cox regression. Multi-omics factor analysis (MOFA) and data integration analysis for biomarker discovery using latent components (DIABLO) were used for multi-omics integration analyses.Results: An automated pipeline was built consisting of 1) miRNA target prediction, 2) correlation analyses between miRNA and its targets on mRNA and protein expression levels, and 3) functional enrichment of correlated targets to identify enriched Kyoto encyclopedia of genes and genomes (KEGG) pathways and gene ontology (GO) terms for a specific miRNA. The pipeline was run for all microRNAs (~700) detected in the TCGA-PAAD cohort. These results can be downloaded from a shiny app (https://emmbor.shinyapps.io/mirfa/). TPS was not altered in pre-diagnostic PDAC patients up to 24 years prior to diagnosis, but increased at diagnosis (OR = 1.03, 95 % CI: 1.01-1.05). Internal area under curves of 0.74, 0.80, and 0.88 were achieved for five metabolites, two proteins, and two miRNAs that were selected by LASSO and bootstrap iterations, in combination with CA 19-9. Neither MOFA nor DIABLO separated well between future PDAC cases and healthy controls. Conclusions: Our bioinformatics pipeline for in silico functional analysis of microRNAs successfully identifies enriched KEGG pathways and GO terms for miRNA isoforms. The investigated plasma samples are heterogeneous, but among the analyzed variables, we identified five metabolites, two proteins, and two microRNAs with highest potential for early PDAC detection. CA 19-9 levels increased closer to diagnosis. We identified five fatty acids that could be studied in a diagnostic PDAC cohort as prognostic biomarkers.
|
|
| 9. |
|
|
| 10. |
- Borgmästars, Emmy, et al.
(författare)
-
Metabolomics for early pancreatic cancer detection in plasma samples from a Swedish prospective population-based biobank
- 2024
-
Ingår i: Journal of Gastrointestinal Oncology. - : AME Publishing Company. - 2078-6891 .- 2219-679X. ; 15:2, s. 755-767
-
Tidskriftsartikel (refereegranskat)abstract
- Background: Pancreatic ductal adenocarcinoma (pancreatic cancer) is often detected at late stages resulting in poor overall survival. To improve survival, more patients need to be diagnosed early when curative surgery is feasible. We aimed to identify circulating metabolites that could be used as early pancreatic cancer biomarkers.Methods: We performed metabolomics by liquid and gas chromatography-mass spectrometry in plasma samples from 82 future pancreatic cancer patients and 82 matched healthy controls within the Northern Sweden Health and Disease Study (NSHDS). Logistic regression was used to assess univariate associations between metabolites and pancreatic cancer risk. Least absolute shrinkage and selection operator (LASSO) logistic regression was used to design a metabolite-based risk score. We used receiver operating characteristic (ROC) analyses to assess the discriminative performance of the metabolite-based risk score.Results: Among twelve risk-associated metabolites with a nominal P value <0.05, we defined a risk score of three metabolites [indoleacetate, 3-hydroxydecanoate (10:0-OH), and retention index (RI): 2,745.4] using LASSO. A logistic regression model containing these three metabolites, age, sex, body mass index (BMI), smoking status, sample date, fasting status, and carbohydrate antigen 19-9 (CA 19-9) yielded an internal area under curve (AUC) of 0.784 [95% confidence interval (CI): 0.714–0.854] compared to 0.681 (95% CI: 0.597–0.764) for a model without these metabolites (P value =0.007). Seventeen metabolites were significantly associated with pancreatic cancer survival [false discovery rate (FDR) <0.1].Conclusions: Indoleacetate, 3-hydroxydecanoate (10:0-OH), and RI: 2,745.4 were identified as the top candidate biomarkers for early detection. However, continued efforts are warranted to determine the usefulness of these metabolites as early pancreatic cancer biomarkers.
|
|
