Skin Cancer Research Goes Digital: Looking for Biomarkers within the Droplets
Abstract
:1. Introduction
2. The ddPCR Method: A Reliable Omics Technology in Oncology
3. The ddPCR Method for Primary Prevention Strategies and Personalized Skin Cancer Screening
4. The ddPCR Nethod Assisting the Prognosis of Skin Cancer
5. DdPCR-Based Liquid Biopsies for Skin Cancer Monitoring and Post-Treatment Follow-Up
5.1. CtDNA Analysis
5.2. Circulating miRNAs Analysis
5.3. CTCs Analysis
5.4. EVs Analysis
6. Discussion
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Technology | Assay | Sensitivity | Specificity | LoD | Type of Alterations | Strengths | Limitations | Ref. |
---|---|---|---|---|---|---|---|---|
Real-time PCR | AS-PCR | 1% | 98% | 0.001% | Know point mutations (SNVs, Fusions, Indels CNVs) | Ease of design and execution; High sensitivity and specificity of detection with fluorescent hydrolysis probes; No need for informatics expert support. | Detects only known genomic variants in limited genomic regions; Reduced multiplexing capability; Quantitation requires standard curve using appropriate positive controls. | [75,76] |
MS-PCR | 0.62% | 89–100% | 0.1% | Known methylation sites | Ease of design and execution; Increased sensitivity when analyzing small quantities of methylated DNA; No need for informatics expert support. | Detects only specific CpG islands. | [76] | |
ddPCR | 0.001–0.1% | 100% | 0.005% | Know point mutations (SNVs, Fusions, Indels, CNVs) | Absolute quantitation possible because of scanning and Poisson-based counting of droplets; No need for a standard curve for quantitation; Short turnaround time; No need for informatics expert support. | Unsuitable for mutation screening and identification of novel variants; Reduced multiplexing Capability. | [68,75,76] | |
NGS | WGS | 5–10% | 80–99.9% | 5–10% | Genome-wide CNVs, DNA methylation studies | Prior knowledge of mutations not required; Genome-wide profiling; Identification of specific cancer signatures. Pathogenic gene screening; Detection of CNVs, fusion genes, rearrangements, neoantigens and TMB. | Extensive bioinformatics support; Variable sensitivity and specificity (increase depth leads to higher costs); Long turnaround time; Costly and not appropriate for patient longitudinal monitoring. | [76] |
WES | 5% | 80–95.6% | 5% | Coding regions, gene promoters, intron-exon junctions, non-coding DNA of miRNA genes | ||||
TargetedNGS gene panels | 0.01–0.1% | 99.6% | 2–5% | Know point mutations | Increased sensitivity and specificity compared to WES/WGS; Produces a smaller and more manageable data set compared to untargeted approaches, making analysis easier. | Less comprehensive than WES/WGS; amplicon methods based on multiplex PCR. | [74] | |
Sanger sequencing | 15–20% | 100% | 20–25% | Know point mutations | Provides sequence information and determines whether a point mutation or small deletion/duplication is present. | Low sensitivity; Low discovery power; Costly and laborious. | [78] |
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Dobre, E.-G.; Constantin, C.; Neagu, M. Skin Cancer Research Goes Digital: Looking for Biomarkers within the Droplets. J. Pers. Med. 2022, 12, 1136. https://doi.org/10.3390/jpm12071136
Dobre E-G, Constantin C, Neagu M. Skin Cancer Research Goes Digital: Looking for Biomarkers within the Droplets. Journal of Personalized Medicine. 2022; 12(7):1136. https://doi.org/10.3390/jpm12071136
Chicago/Turabian StyleDobre, Elena-Georgiana, Carolina Constantin, and Monica Neagu. 2022. "Skin Cancer Research Goes Digital: Looking for Biomarkers within the Droplets" Journal of Personalized Medicine 12, no. 7: 1136. https://doi.org/10.3390/jpm12071136