Optimization of Preanalytical Variables for cfDNA Processing and Detection of ctDNA in Archival Plasma Samples

DNA released from cells into the peripheral blood is known as cell-free DNA (cfDNA), representing a promising noninvasive source of biomarkers that could be utilized to manage Diffuse Large B-Cell Lymphoma (DLBCL), among other diseases. The procedure for purification and handling of cfDNA is not yet standardized, and various preanalytical variables may affect the yield and analysis of cfDNA, including the purification kits, blood collection tubes, and centrifugation regime. Therefore, we aimed to investigate the impact of these preanalytical variables on the yield of cfDNA by comparing three different purification kits DNeasy Blood & Tissue Kit (Qiagen), QIAamp Circulating Nucleic Acid Kit (Qiagen), and Quick-cfDNA Serum & Plasma Kit (Zymo Research). Two blood collection tubes (BCTs), EDTA-K2 and Cell-Free DNA (Streck), stored at four different time points before plasma was separated and cfDNA purified, were compared, and for EDTA tubes, two centrifugation regimes at 2000 × g and 3000 × g were tested. Additionally, we have tested the utility of long-term archival blood samples from DLBCL patients to detect circulating tumor DNA (ctDNA). We observed a higher cfDNA yield using the QIAamp Circulating Nucleic Acid Kit (Qiagen) purification kit, as well as a higher cfDNA yield when blood samples were collected in EDTA BCTs, with a centrifuge regime at 2000 × g. Moreover, ctDNA detection was feasible from archival plasma samples with a median storage time of nine years

Reproducible probe-level analysis of the Affymetrix Exon 1.0 ST array with R/Bioconductor

The presence of different transcripts of a gene across samples can be analysed by whole-transcriptome microarrays. Reproducing results from published microarray data represents a challenge due to the vast amounts of data and the large variety of pre-processing and filtering steps employed before the actual analysis is carried out. To guarantee a firm basis for methodological development where results with new methods are compared with previous results it is crucial to ensure that all analyses are completely reproducible for other researchers. We here give a detailed workflow on how to perform reproducible analysis of the GeneChip Human Exon 1.0 ST Array at probe and probeset level solely in R/Bioconductor, choosing packages based on their simplicity of use. To exemplify the use of the proposed workflow we analyse differential splicing and differential gene expression in a publicly available dataset using various statistical methods. We believe this study will provide other researchers with an easy way of accessing gene expression data at different annotation levels and with the sufficient details needed for developing their own tools for reproducible analysis of the GeneChip Human Exon 1.0 ST Array

Expression of NOTCH3 exon 16 differentiates Diffuse Large B-cell Lymphoma into molecular subtypes and is associated with prognosis

Abstract Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease with diverse clinical presentation and outcome. Bio-clinical prognostic models including oncogene expression and cell-of-origin phenotyping has been developed, however, approximately 30% of all patients still die from their disease, illustrating the need for additional prognostic biomarkers associating oncogenesis and phenotypic subclasses. Hence, we tested if alternative splice variations have biomarker potential. Initial alternative splicing analysis of human exon array from clinical DLBCL samples identified candidate genes. Experimental validation by ddPCR was performed in a DLBCL cohort classified into ABC/GCB subclasses, B-cell associated gene signatures (BAGS: naive, centroblast, centrocyte, memory, and plasmablast), and vincristine resistant gene signatures. Prognostic potential was assessed for aberrantly spliced transcripts. Thus, NOTCH3 was identified as alternatively spliced, with differential exon 16 depletion (−exon 16) between differentiation associated BAGS subtypes. Predicted vincristine resistant patients of the GCB subclass had significantly downregulated NOTCH3 −exon 16 transcript expression and tended to display adverse overall survival for R-CHOP treated patients. In conclusion, we have identified a specific alternatively spliced NOTCH3 event that differentiate molecular subtypes of DLBCL and display prognostic and predictive biomarker potential in GCB DLBCL

Long Non-Coding RNAs in Diffuse Large B-Cell Lymphoma

Diffuse large B-cell lymphoma (DLBCL) is the most common lymphoid malignancy in adults. Although significant progress has been made in recent years to treat DLBCL patients, 30%–40% of the patients eventually relapse or are refractory to first line treatment, calling for better therapeutic strategies for DLBCL. Long non-coding RNAs (lncRNAs) have emerged as a highly diverse group of non-protein coding transcripts with intriguing molecular functions in human disease, including cancer. Here, we review the current understanding of lncRNAs in the pathogenesis and progression of DLBCL to provide an overview of the field. As the current knowledge of lncRNAs in DLBCL is still in its infancy, we provide molecular signatures of lncRNAs in DLBCL cell lines to assist further lncRNA research in DLBCL

Predictive biomarkers in radioresistant rectal cancer:A systematic review

Background and aims: The treatment of locally advanced rectal cancer often consists of neoadjuvant chemoradiotherapy followed by surgery. However, approximately 15% of patients show no response to this neoadjuvant chemoradiotherapy. This systematic review aimed to identify biomarkers of innate radioresistant rectal cancer. Method: Through a systematic literature search, 125 papers were included and analyzed using ROBINS-I, a Cochrane risk of bias tool for non-randomized studies of interventions. Both statistically significant and nonsignificant biomarkers were identified. Biomarkers mentioned more than once in the results or biomarkers with a low or moderate risk of bias were included as the final results. Results: Thirteen unique biomarkers, three genetic signatures, one specific pathway, and two combinations of two or four biomarkers were identified. In particular, the connection between HMGCS2, COASY, and PI3K-pathway seems promising. Future scientific research should focus on further validating these genetic resistance markers