A retrospective study to evaluate the predictive ability of a DNA methylation status-based diagnostic method for the sensitivity of anti-EGFR treatment for metastatic colorectal cancer
Ontology highlight
ABSTRACT:
DISEASE(S): Metastatic Colorectal Cancer
PROVIDER: 2655931 | ecrin-mdr-crc |
REPOSITORIES: ECRIN MDR
Similar Datasets
Project description: Not available
| S-EPMC5104837 | biostudies-literature
Project description:The pathophysiological mechanisms of postoperative delirium (POD) are still not clear, and no reliable biomarker is available to differentiate those with and without POD. Pre- and post-surgery blood from epilepsy subjects undergoing neurosurgery were collected. DNA methylation (DNAm) levels of the TNF gene, IL1B gene, and IL6 gene by the Illumina EPIC array method, and DNAm levels of the TNF gene by pyrosequencing, were analyzed. Blood from 37 subjects were analyzed by the EPIC array method, and blood from 27 subjects were analyzed by pyrosequencing. Several CpGs in the TNF gene in preoperative blood showed a negative correlation between their DNAm and age both in the POD group and in the non-POD group. However, these negative correlations were observed only in the POD group after neurosurgery. Neurosurgery significantly altered DNAm levels at 17 out of 24 CpG sites on the TNF gene, 8 out of 14 CpG sites on the IL1B gene, and 4 out of 14 CpG sites on the IL6 gene. Furthermore, it was found that the Inflammatory Methylation Index (IMI), which was based on the post-surgery DNAm levels at the selected five CpG sites, can be a potential detection tool for delirium with moderate accuracy; area under the curve (AUC) value was 0.84. The moderate accuracy of this IMI was replicated using another cohort from our previous study, in which the AUC was 0.79. Our findings provide further evidence of the potential role of epigenetics and inflammation in the pathophysiology of delirium.
| S-EPMC8660911 | biostudies-literature
Project description:Osteosarcoma is the most common primary malignant bone tumor in children. Validated biological markers for disease prognosis available at diagnosis are lacking. No genome-wide DNA methylation studies linked to clinical outcomes have been reported in osteosarcoma to the best of our knowledge. To address this, we tested the methylome at over 1.1 million loci in 15 osteosarcoma biopsy samples obtained prior to the initiation of therapy and correlated these molecular data with disease outcomes. At more than 17% of the tested loci, samples obtained from patients who experienced disease relapse were more methylated than those from patients who did not have recurrence while patients who did not experience disease relapse had more DNA methylation at fewer than 1%. In samples from patients who went on to have recurrent disease, increased DNA methylation was found at gene bodies, intergenic regions and empirically-annotated candidate enhancers, whereas candidate gene promoters were unusual for a more balanced distribution of increased and decreased DNA methylation with 6.6% of gene promoter loci being more methylated and 2% of promoter loci being less methylated in patients with disease relapse. A locus at the TLR4 gene demonstrates one of strongest associations between DNA methylation and 5 y event-free survival (P-value = 1.7 × 10(-6)), with empirical annotation of this locus showing promoter characteristics. Our data indicate that DNA methylation information has the potential to be predictive of outcome in pediatric osteosarcoma, and that both promoters and non-promoter loci are potentially informative in DNA methylation studies.
| S-EPMC4622015 | biostudies-literature
Project description:AIMS:EGFR mutations now guide the clinical use of EGFR-targeted therapy in lung cancer. However, standard EGFR mutation analysis requires a minimum amount of tumor tissue, which may not be available in certain situations. In this study, we combined a mass spectrometry genotyping assay (Sequenom) with a mutant-enriched PCR (ME-PCR) to detect EGFR mutations in free plasma DNA from patients with lung cancer. METHOD:DNAs were extracted from 31 plasma samples from 31 patients and analyzed by both methods for EGFR Exon 19 deletion and EGFR L858R mutation. Results in plasma DNA samples were compared with EGFR mutation status obtained in tumor DNA (18/31 EGFR mutant). The relationship of EGFR mutation status in tumor and/or plasma samples to overall survival was assessed. RESULTS:The EGFR mutation status in plasma DNA was identical to the primary tumor in 61% of patients (19/31). By mass spectrometry genotyping, the plasma samples contained mutant DNA corresponding to 5/14 EGFR Exon 19 deletions and 3/4 EGFR L858R mutations previously diagnosed in the matched tumors. Two samples were positive in plasma DNA but negative in primary tumor tissue. Results were similar for samples studied by ME-PCR. For patients treated with erlotinib, overall survival was correlated with the presence of EGFR mutation in plasma and/or tumor tissue (p=0.002), with the two patients positive only in plasma DNA showing responses and favorable outcomes. CONCLUSION:The detection of EGFR mutations in plasma DNA samples by mass spectrometry genotyping and ME-PCR is feasible. A positive EGFR result in plasma DNA has a high predictive value for tumor EGFR status and for favorable clinical course on EGFR-targeted therapy and could therefore be useful in guiding clinical decisions in patients with insufficient or unavailable tumor specimens.
| S-EPMC3282180 | biostudies-literature
Project description:Early diagnosis of colorectal cancer (CRC) is of high importance as prognosis depends on tumour stage at the time of diagnosis. Detection of tumour-specific DNA methylation marks in cfDNA has several advantages over other approaches and has great potential for solving diagnostic needs. We report here the identification of DNA methylation biomarkers for CRC and give insights in our methylation-sensitive restriction enzyme coupled qPCR (MSRE-qPCR) system. Targeted microarrays were used to investigate the DNA methylation status of 360 cancer-associated genes. Validation was done by qPCR-based approaches. A focus was on investigating marker performance in cfDNA from 88 patients (44 CRC, 44 controls). Finally, the workflow was scaled-up to perform 180plex analysis on 110 cfDNA samples, to identify a DNA methylation signature for advanced colonic adenomas (AA). A DNA methylation signature (n = 44) was deduced from microarray experiments and confirmed by quantitative methylation-specific PCR (qMSP) and by MSRE-qPCR, providing for six genes' single areas under the curve (AUC) values of >0.85 (WT1, PENK, SPARC, GDNF, TMEFF2, DCC). A subset of the signatures can be used for patient stratification and therapy monitoring for progressed CRC with liver metastasis using cfDNA. Furthermore, we identified a 35-plex classifier for the identification of AAs with an AUC of 0.80.
| S-EPMC8929153 | biostudies-literature
Project description:Molecular analysis of the mutation status for EGFR and KRAS are now routine in the management of non-small cell lung cancer. Radiogenomics, the linking of medical images with the genomic properties of human tumors, provides exciting opportunities for non-invasive diagnostics and prognostics. We investigated whether EGFR and KRAS mutation status can be predicted using imaging data. To accomplish this, we studied 186 cases of NSCLC with preoperative thin-slice CT scans. A thoracic radiologist annotated 89 semantic image features of each patient's tumor. Next, we built a decision tree to predict the presence of EGFR and KRAS mutations. We found a statistically significant model for predicting EGFR but not for KRAS mutations. The test set area under the ROC curve for predicting EGFR mutation status was 0.89. The final decision tree used four variables: emphysema, airway abnormality, the percentage of ground glass component and the type of tumor margin. The presence of either of the first two features predicts a wild type status for EGFR while the presence of any ground glass component indicates EGFR mutations. These results show the potential of quantitative imaging to predict molecular properties in a non-invasive manner, as CT imaging is more readily available than biopsies.
| S-EPMC5282551 | biostudies-literature
Project description:Rapid and reliable detection of disease-associated DNA methylation patterns has major potential to advance molecular diagnostics and underpin research investigations. We describe the development and validation of minimal methylation classifier (MIMIC), combining CpG signature design from genome-wide datasets, multiplex-PCR and detection by single-base extension and MALDI-TOF mass spectrometry, in a novel method to assess multi-locus DNA methylation profiles within routine clinically-applicable assays. We illustrate the application of MIMIC to successfully identify the methylation-dependent diagnostic molecular subgroups of medulloblastoma (the most common malignant childhood brain tumour), using scant/low-quality samples remaining from the most recently completed pan-European medulloblastoma clinical trial, refractory to analysis by conventional genome-wide DNA methylation analysis. Using this approach, we identify critical DNA methylation patterns from previously inaccessible cohorts, and reveal novel survival differences between the medulloblastoma disease subgroups with significant potential for clinical exploitation.
| S-EPMC5647382 | biostudies-literature
Project description:DNA Methylation profiles were generated for retrospective cases to support work into investigation of the immune environment in pediatric ependymoma. Samples were analyzed using the Illumina 450k beadchip and processed using the Heidelberg classifier (v11.2b and subsequently v12.3 for subgrouping/subtyping). The aim of the study was to better understand the immune-tumor microenvironment in pediatric ependymoma and the methylation profiles support the diagnoses of each case.
2023-10-02 | GSE190798 | GEO
Project description:Glioblastoma (GBM) is the most frequent and aggressive primary brain tumor in adults. Recently, we demonstrated that plasma denaturation profiles of glioblastoma patients obtained using Differential Scanning Fluorimetry can be automatically distinguished from healthy controls with the help of Artificial Intelligence (AI). Here, we used a set of machine-learning algorithms to automatically classify plasma denaturation profiles of glioblastoma patients according to their EGFR status. We found that Adaboost AI is able to discriminate EGFR alterations in GBM with an 81.5% accuracy. Our study shows that the use of these plasma denaturation profiles could answer the unmet neuro-oncology need for diagnostic predictive biomarker in combination with brain MRI and clinical data, in order to allow for a rapid orientation of patients for a definitive pathological diagnosis and then treatment. We complete this study by showing that discriminating another mutation, MGMT, seems harder, and that post-surgery monitoring using our approach is not conclusive in the 48 h that follow the surgery.
| S-EPMC9913157 | biostudies-literature
Project description:Prior research has established an electrochemical method based on the differential adsorption capacity of gold surfaces with different methylated DNA degrees and found that this method might be valuable for cancer diagnosis by detecting circulating free DNA methylation. However, further investigation on the underlying mechanism and validation of its diagnostic and prognostic values in a large cohort of malignant tumors was limited. We found that DNA with different methylation levels formed particles of diverse sizes on the gold surface. Hydrophobic bonds played a significant role in the binding process of methylated DNA to the gold surface. The detection condition of an adsorption time of 10 min and temperature of 20 °C was optimal. In a large cohort of plasma samples from the patients with different malignant tumors, as well as normal individuals, we found that the electrochemical detection method based on the differential adsorption capacity of methylated DNA degree on a gold surface could be used as a noninvasive tool for malignant tumor diagnosis and prognostic evaluation. The diagnostic efficiency of this method in malignant tumors was even slightly better than that of the current tumor biomarkers widely used in routine clinical practice (circulating free DNA (cfDNA) vs. carcinoembryonic antigen (CEA), 0.8131 vs. 0.7191 and cfDNA vs. CA19-9, 0.7687 vs. 0.6693).
| S-EPMC7914975 | biostudies-literature