Concentration and Methylation of Cell-Free DNA from Blood Plasma as Diagnostic Markers of Renal Cancer

Skrypkina, Inessa; Tsyba, L. O.; Onyshchenko, Kateryna; Morderer, Dmytro; Kashparova, O.; Nikolaienko, Oleksii; Панасенко, Г. В.; Возіанов, С. О.; Romanenko, Alina; Rynditch, A. V.

doi:10.1155/2016/3693096

Cited by 60 publications

(62 citation statements)

References 68 publications

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“…Less invasive sample types such as blood or urine were investigated in six studies [5,36,[38][39][40][41], with three (16%) studies reporting on blood [38,39,41], one (5%) describing both blood and urine [40], and two (11%) reporting on urine in addition to tissue [5,36]. Not every marker was independently validated in the same specimen type; five single urine biomarkers and nine single blood biomarkers were independently studied.…”

Section: Findings In Liquid Biopsiesmentioning

confidence: 99%

“…Other single-marker sensitivities were low, ranging from 0% for VHL or MGMT methylation in blood (specificities 100% and 97%, respectively) to 65% for RASSF1A in urine (specificity 89%). Moreover, sensitivities for the same marker greatly varied: for example, sensitivities for RASSF1A ranged from 11% to 62.9% in blood (specificities of 93-98%) [38][39][40][41] and from 50% to 65% in urine (specificities 89-100%) [5,40]; sensitivities for TIMP3 in blood ranged from 17% to 57% (specificities 61-100%) [39,40] and from 46% to 52% in urine (specificities 91-100%) [5,40]. Importantly, several genomic locations for the same biomarkers were investigated across these studies.…”

Section: Findings In Liquid Biopsiesmentioning

confidence: 99%

“…3). LRRC3B (74% sensitivity, 66.7% specificity) [41] appeared to be the most promising individual marker, but independent validation is lacking ( Supplementary Fig. 1).…”

Section: Findings In Liquid Biopsiesmentioning

confidence: 99%

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Diagnostic DNA Methylation Biomarkers for Renal Cell Carcinoma: A Systematic Review

Lommen

Vaes

Aarts

et al. 2021

European Urology Oncology

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Context: The 5-yr survival of early-stage renal cell carcinoma (RCC) is approximately 93%, but once metastasised, the 5-yr survival plummets to 12%, indicating that early RCC detection is crucial to improvement in survival. DNA methylation biomarkers have been suggested to be of potential diagnostic value; however, their current state of clinical translation is unclear and a comprehensive overview is lacking. Objective: To systematically review and summarise all literature regarding diagnostic DNA methylation biomarkers for RCC. Evidence acquisition: We performed a systematic literature review of PubMed, EMBASE, Medline, and Google Scholar up to January 2019, according to the Preferred Reporting Items for Systematic Review and Meta-Analysis of Diagnostic Test Accuracy Studies (PRISMA-DTA) guidelines. Included studies were scored according to the Standards for Reporting of Diagnostic Accuracy Studies (STARD) criteria. Forest plots were generated to summarise diagnostic performance of all biomarkers. Level of evidence (LoE) and potential risk of bias were determined for all included studies. Evidence synthesis: After selection, 19 articles reporting on 44 diagnostic DNA methylation biomarkers and 11 multimarker panels were included; however, only 15 biomarkers were independently validated. STARD scores varied from 4 to 13 out of 23 points, with a median of 10 points. Large variation in subgroups, methods, and primer locations was observed. None of the reported biomarkers exceeded LoE III, and the majority of studies reported inadequately. Conclusions: None of the reported biomarkers exceeded LoE III, indicating their limited clinical utility. Moreover, study reproducibility and further development of these RCC biomarkers are greatly hampered by inadequate reporting.

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Section: Findings In Liquid Biopsiesmentioning

confidence: 99%

Section: Findings In Liquid Biopsiesmentioning

confidence: 99%

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Diagnostic DNA Methylation Biomarkers for Renal Cell Carcinoma: A Systematic Review

Lommen

Vaes

Aarts

et al. 2021

European Urology Oncology

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“…However, eTumorType is capable of discriminating cancer types, which is beyond the current studies on CTC or cfDNA data, as the majority of these studies only focused on finding features ( e.g. , CNV, mutation, microRNA, methylation, and gene expression) associated with specific cancer types [25], [26], [27], [28], [29], [30]. To our best knowledge, no such algorithms have been developed so far.…”

Section: Discussionmentioning

confidence: 99%

eTumorType, An Algorithm of Discriminating Cancer Types for Circulating Tumor Cells or Cell-Free DNAs in Blood

Zou

Wang

2017

Genomics, Proteomics &Amp; Bioinformatics

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With the technology development on detecting circulating tumor cells (CTCs) and cell-free DNAs (cfDNAs) in blood, serum, and plasma, non-invasive diagnosis of cancer becomes promising. A few studies reported good correlations between signals from tumor tissues and CTCs or cfDNAs, making it possible to detect cancers using CTCs and cfDNAs. However, the detection cannot tell which cancer types the person has. To meet these challenges, we developed an algorithm, eTumorType, to identify cancer types based on copy number variations (CNVs) of the cancer founding clone. eTumorType integrates cancer hallmark concepts and a few computational techniques such as stochastic gradient boosting, voting, centroid, and leading patterns. eTumorType has been trained and validated on a large dataset including 18 common cancer types and 5327 tumor samples. eTumorType produced high accuracies (0.86–0.96) and high recall rates (0.79–0.92) for predicting colon, brain, prostate, and kidney cancers. In addition, relatively high accuracies (0.78–0.92) and recall rates (0.58–0.95) have also been achieved for predicting ovarian, breast luminal, lung, endometrial, stomach, head and neck, leukemia, and skin cancers. These results suggest that eTumorType could be used for non-invasive diagnosis to determine cancer types based on CNVs of CTCs and cfDNAs.

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“…biomarker. Methylation levels of CpG islands of RASSF1A, FHIT, and APC genes has been detected in cfDNA of renal cancer patients[30]. Combined analysis of methylation frequency of multiple genes reach a sensitivity of 62,9% and a specificity of 87%.…”

mentioning

confidence: 99%

Emerging Molecular Technologies in Renal Cell Carcinoma: Liquid Biopsy

Cimadamore¹,

Gasparrini²,

Massari³

et al. 2018

Preprint

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Liquid biopsy, based on the circulating tumor cells (CTCs) and cell-free nucleic acids has potential applications at multiple points throughout the natural course of cancer, from diagnosis to follow-up. The advantages of doing ctDNA assessment vs. tissue-based genomic profile are the minimal procedural risk, the possibility to serial testing in order to monitor disease-relapse and response to therapy over time and to reduce hospitalization costs during the entire process. However some critical issues related to ctDNA assays should be taken in consideration. The sensitivity of ctDNA assays depends on the assessment technique and genetic platforms used, on tumor-organ, stage, tumor heterogeneity, tumor clonality. The specificity is usually very high, whereas the concordance with tumor-based biopsy is generally low. In patients with renal cell carcinoma (RCC) qualitative analyses of ctDNA have been performed with interesting results regarding selective pressure from therapy, therapeutic resistance, exceptional treatment response to everolimus and mutations associated with aggressive behavior. Quantitative analyses showed variations of cfDNA levels at different tumor stage. Compared to CTC assay, ctDNA is more stable than cells and easier to isolate. Splice variants, information at single-cell level and functional assays along with proteomics, transcriptomics and metabolomics studies can be performed only in CTCs.

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Concentration and Methylation of Cell-Free DNA from Blood Plasma as Diagnostic Markers of Renal Cancer

Cited by 60 publications

References 68 publications

Diagnostic DNA Methylation Biomarkers for Renal Cell Carcinoma: A Systematic Review

Diagnostic DNA Methylation Biomarkers for Renal Cell Carcinoma: A Systematic Review

eTumorType, An Algorithm of Discriminating Cancer Types for Circulating Tumor Cells or Cell-Free DNAs in Blood

Emerging Molecular Technologies in Renal Cell Carcinoma: Liquid Biopsy

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