Diagnostic accuracy of liquid biopsy in endometrial cancer

Background: Liquid biopsy is a minimally invasive collection of a patient body fluid sample. In oncology, they offer several advantages compared to traditional tissue biopsies. However, the potential of this method in endometrial cancer (EC) remains poorly explored. We studied the utility of tumor educated platelets (TEPs) and circulating tumor DNA (ctDNA) for preoperative EC diagnosis, including histology determination. Methods: TEPs from 295 subjects (53 EC patients, 38 patients with benign gynecologic conditions, and 204 healthy women) were RNA-sequenced. DNA sequencing data were obtained for 519 primary tumor tissues and 16 plasma samples. Artificial intelligence was applied to sample classification. Results: Platelet-dedicated classifier yielded AUC of 97.5% in the test set when discriminating between healthy subjects and cancer patients. However, the discrimination between endometrial cancer and benign gynecologic conditions was more challenging, with AUC of 84.1%. ctDNA-dedicated classifier discriminated primary tumor tissue samples with AUC of 96% and ctDNA blood samples with AUC of 69.8%. Conclusions: Liquid biopsies show potential in EC diagnosis. Both TEPs and ctDNA profiles coupled with artificial intelligence constitute a source of useful information. Further work involving more cases is warranted.publishedVersio

Fluorescence activated cell sorting followed by small RNA sequencing reveals stable microRNA expression during cell cycle progression.

BACKGROUND: Previously, drug-based synchronization procedures were used for characterizing the cell cycle dependent transcriptional program. However, these synchronization methods result in growth imbalance and alteration of the cell cycle machinery. DNA content-based fluorescence activated cell sorting (FACS) is able to sort the different cell cycle phases without perturbing the cell cycle. MiRNAs are key transcriptional regulators of the cell cycle, however, their expression dynamics during cell cycle has not been explored. METHODS: Following an optimized FACS, a complex initiative of high throughput platforms (microarray, Taqman Low Density Array, small RNA sequencing) were performed to study gene and miRNA expression profiles of cell cycle sorted human cells originating from different tissues. Validation of high throughput data was performed using quantitative real time PCR. Protein expression was detected by Western blot. Complex statistics and pathway analysis were also applied. RESULTS: Beyond confirming the previously described cell cycle transcriptional program, cell cycle dependently expressed genes showed a higher expression independently from the cell cycle phase and a lower amplitude of dynamic changes in cancer cells as compared to untransformed fibroblasts. Contrary to mRNA changes, miRNA expression was stable throughout the cell cycle. CONCLUSIONS: Cell cycle sorting is a synchronization-free method for the proper analysis of cell cycle dynamics. Altered dynamic expression of universal cell cycle genes in cancer cells reflects the transformed cell cycle machinery. Stable miRNA expression during cell cycle progression may suggest that dynamical miRNA-dependent regulation may be of less importance in short term regulations during the cell cycle

Genetic variation in metronidazole metabolism and oxidative stress pathways in clinical Giardia lamblia assemblage A and B isolates

Christina S Saghaug,1,2 Christian Klotz,3 Juha P Kallio,4 Hans-Richard Brattbakk,1,5 Tomasz Stokowy,1,5 Toni Aebischer,3 Inari Kursula,4,6 Nina Langeland,1–2,7 Kurt Hanevik1,21Department of Clinical Science, University of Bergen, Bergen, Hordaland, Norway; 2Norwegian National Advisory Unit on Tropical Infectious Diseases, Department of Medicine, Haukeland University Hospital, Bergen, Hordaland, Norway; 3Department of Infectious Diseases, Unit 16 Mycotic and Parasitic Agents and Mycobacteria, Robert Koch-Institute, Berlin, Germany; 4Department of Biomedicine, University of Bergen, Bergen, Hordaland, Norway; 5Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, Bergen, Hordaland, Norway; 6Biocenter Oulu and Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland; 7Department of Medicine, Haraldsplass Deaconess Hospital, Bergen, Hordaland, NorwayPurpose: Treatment-refractory Giardia cases have increased rapidly within the last decade. No markers of resistance nor a standardized susceptibility test have been established yet, but several enzymes and their pathways have been associated with metronidazole (MTZ) resistant Giardia. Very limited data are available regarding genetic variation in these pathways. We aimed to investigate genetic variation in metabolic pathway genes proposed to be involved in MTZ resistance in recently acquired, cultured clinical isolates.Methods: Whole genome sequencing of 12 assemblage A2 and 8 assemblage B isolates was done, to decipher genomic variation in Giardia. Twenty-nine genes were identified in a literature search and investigated for their single nucleotide variants (SNVs) in the coding/non-coding regions of the genes, either as amino acid changing (non-synonymous SNVs) or non-changing SNVs (synonymous).Results: In Giardia assemblage B, several genes involved in MTZ activation or oxidative stress management were found to have higher numbers of non-synonymous SNVs (thioredoxin peroxidase, nitroreductase 1, ferredoxin 2, NADH oxidase, nitroreductase 2, alcohol dehydrogenase, ferredoxin 4 and ferredoxin 1) than the average variation. For Giardia assemblage A2, the highest genetic variability was found in the ferredoxin 2, ferredoxin 6 and in nicotinamide adenine dinucleotide phosphate (NADPH) oxidoreductase putative genes. SNVs found in the ferredoxins and nitroreductases were analyzed further by alignment and homology modeling. SNVs close to the iron-sulfur cluster binding sites in nitroreductase-1 and 2 and ferredoxin 2 and 4 could potentially affect protein function. Flavohemoprotein seems to be a variable-copy gene, due to higher, but variable coverage compared to other genes investigated.Conclusion: In clinical Giardia isolates, genetic variability is common in important genes in the MTZ metabolizing pathway and in the management of oxidative and nitrosative stress and includes high numbers of non-synonymous SNVs. Some of the identified amino acid changes could potentially affect the respective proteins important in the MTZ metabolism.Keywords: drug metabolism, resistance, genetic analysis, metronidazole genes, ferredoxin, genetic diversit

Transcriptomic landscape of blood platelets in healthy donors

Blood platelet RNA-sequencing is increasingly used among the scientific community. Aberrant platelet transcriptome is common in cancer or cardiovascular disease, but reference data on platelet RNA content in healthy individuals are scarce and merit complex investigation. We sought to explore the dynamics of platelet transcriptome. Datasets from 204 healthy donors were used for the analysis of splice variants, particularly with regard to age, sex, blood storage time, unit of collection or library size. Genes B2M, PPBP, TMSB4X, ACTB, FTL, CLU, PF4, F13A1, GNAS, SPARC, PTMA, TAGLN2, OAZ1 and OST4 demonstrated the highest expression in the analysed cohort, remaining substantial transcription consistency. CSF3R gene was found upregulated in males (fold change 2.10, FDR q < 0.05). Cohort dichotomisation according to the median age, showed upregulated KSR1 in the older donors (fold change 2.11, FDR q < 0.05). Unsupervised hierarchical clustering revealed two clusters which were irrespective of age, sex, storage time, collecting unit or library size. However, when donors are analysed globally (as vectors), sex, storage time, library size, the unit of blood collection as well as age impose a certain degree of between- and/or within-group variability. Healthy donor platelet transcriptome retains general consistency, with very few splice variants deviating from the landscape. Although multidimensional analysis reveals statistically significant variability between and within the analysed groups, biologically, these changes are minor and irrelevant while considering disease classification. Our work provides a reference for studies working both on healthy platelets and pathological conditions affecting platelet transcriptome

Genetic variation in metronidazole metabolism and oxidative stress pathways in clinical Giardia lamblia assemblage A and B isolates

Abstract Purpose: Treatment-refractory Giardia cases have increased rapidly within the last decade. No markers of resistance nor a standardized susceptibility test have been established yet, but several enzymes and their pathways have been associated with metronidazole (MTZ) resistant Giardia. Very limited data are available regarding genetic variation in these pathways. We aimed to investigate genetic variation in metabolic pathway genes proposed to be involved in MTZ resistance in recently acquired, cultured clinical isolates. Methods: Whole genome sequencing of 12 assemblage A2 and 8 assemblage B isolates was done, to decipher genomic variation in Giardia. Twenty-nine genes were identified in a literature search and investigated for their single nucleotide variants (SNVs) in the coding/non-coding regions of the genes, either as amino acid changing (non-synonymous SNVs) or non-changing SNVs (synonymous). Results: In Giardia assemblage B, several genes involved in MTZ activation or oxidative stress management were found to have higher numbers of non-synonymous SNVs (thioredoxin peroxidase, nitroreductase 1, ferredoxin 2, NADH oxidase, nitroreductase 2, alcohol dehydrogenase, ferredoxin 4 and ferredoxin 1) than the average variation. For Giardia assemblage A2, the highest genetic variability was found in the ferredoxin 2, ferredoxin 6 and in nicotinamide adenine dinucleotide phosphate (NADPH) oxidoreductase putative genes. SNVs found in the ferredoxins and nitroreductases were analyzed further by alignment and homology modeling. SNVs close to the iron-sulfur cluster binding sites in nitroreductase-1 and 2 and ferredoxin 2 and 4 could potentially affect protein function. Flavohemoprotein seems to be a variable-copy gene, due to higher, but variable coverage compared to other genes investigated. Conclusion: In clinical Giardia isolates, genetic variability is common in important genes in the MTZ metabolizing pathway and in the management of oxidative and nitrosative stress and includes high numbers of non-synonymous SNVs. Some of the identified amino acid changes could potentially affect the respective proteins important in the MTZ metabolism

imPlatelet classifier: image-converted RNA biomarker profiles enable blood-based cancer diagnostics

Liquid biopsies offer a minimally invasive sample collection, outperforming traditional biopsies employed for cancer evaluation. The widely used material is blood, which is the source of tumor-educated platelets. Here, we developed the imPlatelet classifier, which converts RNA-sequenced platelet data into images in which each pixel corresponds to the expression level of a certain gene. Biological knowledge from the Kyoto Encyclopedia of Genes and Genomes was also implemented to improve accuracy. Images obtained from samples can then be compared against standard images for specific cancers to determine a diagnosis. We tested imPlatelet on a cohort of 401 non-small cell lung cancer patients, 62 sarcoma patients, and 28 ovarian cancer patients. imPlatelet provided excellent discrimination between lung cancer cases and healthy controls, with accuracy equal to 1 in the independent dataset. When discriminating between noncancer cases and sarcoma or ovarian cancer patients, accuracy equaled 0.91 or 0.95, respectively, in the independent datasets. According to our knowledge, this is the first study implementing an image-based deep-learning approach combined with biological knowledge to classify human samples. The performance of imPlatelet considerably exceeds previously published methods and our own alternative attempts of sample discrimination. We show that the deep-learning image-based classifier accurately identifies cancer, even when a limited number of samples are available