Early diagnosis of ovarian cancer based on methylation profiles in peripheral blood cell-free DNA: a systematic review

Abstract Patients diagnosed with epithelial ovarian cancer (OC) have a 5-year survival rate of 49%. For early-stage disease, the 5-year survival rate is above 90%. However, advanced-stage disease accounts for most cases as patients with early stages often are asymptomatic or present with unspecific symptoms, highlighting the need for diagnostic tools for early diagnosis. Liquid biopsy is a minimal invasive blood-based approach that utilizes circulating tumor DNA (ctDNA) shed from tumor cells for real-time detection of tumor genetics and epigenetics. Increased DNA methylation of promoter regions is an early event during tumorigenesis, and the methylation can be detected in ctDNA, accentuating the promise of methylated ctDNA as a biomarker for OC diagnosis. Many studies have investigated multiple methylation biomarkers in ctDNA from plasma or serum for discriminating OC patients from patients with benign diseases of the ovaries and/or healthy females. This systematic review summarizes and evaluates the performance of the currently investigated DNA methylation biomarkers in blood-derived ctDNA for early diagnosis of OC. PubMed’s MEDLINE and Elsevier’s Embase were systematically searched, and essential results such as methylation frequency of OC cases and controls, performance measures, as well as preanalytical factors were extracted. Overall, 29 studies met the inclusion criteria for this systematic review. The most common method used for methylation analysis was methylation-specific PCR, with half of the studies using plasma and the other half using serum. RASSF1A, BRCA1, and OPCML were the most investigated gene-specific methylation biomarkers, with OPCML having the best performance measures. Generally, methylation panels performed better than single gene-specific methylation biomarkers, with one methylation panel of 103,456 distinct regions and 1,116,720 CpGs having better performance in both training and validation cohorts. However, the evidence is still limited, and the promising methylation panels, as well as gene-specific methylation biomarkers highlighted in this review, need validation in large, prospective cohorts with early-stage asymptomatic OC patients to assess the true diagnostic value in a clinical setting

Early diagnosis of ovarian cancer based on methylation profiles in peripheral blood cell-free DNA: a systematic review

Abstract Patients diagnosed with epithelial ovarian cancer (OC) have a 5-year survival rate of 49%. For early-stage disease, the 5-year survival rate is above 90%. However, advanced-stage disease accounts for most cases as patients with early stages often are asymptomatic or present with unspecific symptoms, highlighting the need for diagnostic tools for early diagnosis. Liquid biopsy is a minimal invasive blood-based approach that utilizes circulating tumor DNA (ctDNA) shed from tumor cells for real-time detection of tumor genetics and epigenetics. Increased DNA methylation of promoter regions is an early event during tumorigenesis, and the methylation can be detected in ctDNA, accentuating the promise of methylated ctDNA as a biomarker for OC diagnosis. Many studies have investigated multiple methylation biomarkers in ctDNA from plasma or serum for discriminating OC patients from patients with benign diseases of the ovaries and/or healthy females. This systematic review summarizes and evaluates the performance of the currently investigated DNA methylation biomarkers in blood-derived ctDNA for early diagnosis of OC. PubMed’s MEDLINE and Elsevier’s Embase were systematically searched, and essential results such as methylation frequency of OC cases and controls, performance measures, as well as preanalytical factors were extracted. Overall, 29 studies met the inclusion criteria for this systematic review. The most common method used for methylation analysis was methylation-specific PCR, with half of the studies using plasma and the other half using serum. RASSF1A, BRCA1, and OPCML were the most investigated gene-specific methylation biomarkers, with OPCML having the best performance measures. Generally, methylation panels performed better than single gene-specific methylation biomarkers, with one methylation panel of 103,456 distinct regions and 1,116,720 CpGs having better performance in both training and validation cohorts. However, the evidence is still limited, and the promising methylation panels, as well as gene-specific methylation biomarkers highlighted in this review, need validation in large, prospective cohorts with early-stage asymptomatic OC patients to assess the true diagnostic value in a clinical setting

Mutational landscape of immune surveillance genes in diffuse large B-cell lymphoma

Immune surveillance is the dynamic process whereby the immune system identifies and kills tumor cells based on their aberrant expression of stress-related surface molecules or presentation of tumor neoantigens. It plays a crucial role in controlling the initiation and progression of hematologic cancers such as leukemia and lymphoma, and it has been reported that diffuse large B-cell lymphoma (DLBCL) fails to express specific cell-surface molecules that are necessary for the recognition and elimination of tumor cells. This review is based on a systematic search strategy to identify relevant literature in the PubMed and Embase databases. Ten candidate genes are identified based on mutational frequency, and functions with detailed mapping performed for hotspot alterations that may have a functional impact on malignant transformation and decreased immune surveillance efficacy. Ongoing development of technology and bioinformatics tools combined with data from large clinical cohorts have the potential to define the mutational landscape associated with immune surveillance in DLBCL. Specific functional studies are required to make an unambiguous link between genetic aberrations and biological impact on impaired immune surveillance

Additional file 3: of Differential effect of surgical manipulation on gene expression in normal breast tissue and breast tumor tissue

GSEA enrichment plots â interaction. GSEA enrichment plots of pathways significantly affected by interaction. (TIF 3730 kb

Mutational landscape of immune surveillance genes in diffuse large B-cell lymphoma

Immune surveillance is the dynamic process whereby the immune system identifies and kills tumor cells based on their aberrant expression of stress-related surface molecules or presentation of tumor neoantigens. It plays a crucial role in controlling the initiation and progression of hematologic cancers such as leukemia and lymphoma, and it has been reported that diffuse large B-cell lymphoma (DLBCL) fails to express specific cell-surface molecules that are necessary for the recognition and elimination of tumor cells. This review is based on a systematic search strategy to identify relevant literature in the PubMed and Embase databases. Ten candidate genes are identified based on mutational frequency, and functions with detailed mapping performed for hotspot alterations that may have a functional impact on malignant transformation and decreased immune surveillance efficacy. Ongoing development of technology and bioinformatics tools combined with data from large clinical cohorts have the potential to define the mutational landscape associated with immune surveillance in DLBCL. Specific functional studies are required to make an unambiguous link between genetic aberrations and biological impact on impaired immune surveillance

Additional file 11: of Differential effect of surgical manipulation on gene expression in normal breast tissue and breast tumor tissue

GSEA enrichment plots â tissue type. GSEA enrichment plots of pathways significantly affected by tissue type. (TIF 3588 kb

Additional file 9: of Differential effect of surgical manipulation on gene expression in normal breast tissue and breast tumor tissue

Top 50 up-regulated genes (tissue type). The top 50 genes up-regulated in the GEE tissue type analysis. (PDF 35 kb

Additional file 3: of Differential effect of surgical manipulation on gene expression in normal breast tissue and breast tumor tissue

GSEA enrichment plots â interaction. GSEA enrichment plots of pathways significantly affected by interaction. (TIF 3730 kb

Additional file 10: of Differential effect of surgical manipulation on gene expression in normal breast tissue and breast tumor tissue

Top 50 down-regulated genes (tissue type). The top 50 genes down-regulated in the GEE tissue type analysis. (PDF 35 kb