Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1
Vincent Camus
 + 3082 word(s) 3082 2021-03-09 09:15:54 |
2 update layout and reference
Rita Xu
 Meta information modification 3082 2021-03-10 04:01:37 |

Video Upload Options

We provide professional Video Production Services to translate complex research into visually appealing presentations. Would you like to try it?
No, upload directly Yes

Confirm

Are you sure to Delete?
Yes No
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Camus, V. Cell-Free DNA. Encyclopedia. Available online: https://encyclopedia.pub/entry/7852 (accessed on 17 November 2024).
Camus V. Cell-Free DNA. Encyclopedia. Available at: https://encyclopedia.pub/entry/7852. Accessed November 17, 2024.
Camus, Vincent. "Cell-Free DNA" Encyclopedia, https://encyclopedia.pub/entry/7852 (accessed November 17, 2024).
Camus, V. (2021, March 09). Cell-Free DNA. In Encyclopedia. https://encyclopedia.pub/entry/7852
Camus, Vincent. "Cell-Free DNA." Encyclopedia. Web. 09 March, 2021.
Cell-Free DNA
Edit
This entry is adapted from the peer-reviewed paper 10.3390/ph14030207

Cell-free DNA (cfDNA) testing, is an emerging “liquid biopsy” tool for noninvasive lymphoma detection, and an increased amount of data are now available to use this technique with accuracy, especially in classical Hodgkin lymphoma (cHL) and diffuse large B-cell lymphoma (DLBCL). The advantages of cfDNA include simplicity of repeated blood sample acquisition over time; dynamic, noninvasive, and quantitative analysis; fast turnover time; reasonable cost; and established consistency with results from tumor genomic DNA. cfDNA analysis offers an easy method for genotyping the overall molecular landscape of pediatric and adult cHL and may help in cases of diagnostic difficulties between cHL and other lymphomas.

cell-free DNA Hodgkin lymphoma precision medicine liquid biopsy circulating tumor DNA

1. Introduction

1.1. Classical Hodgkin’s Lymphoma (cHL) Particularities

Classical Hodgkin’s lymphoma (cHL) is a rare and curable malignancy with an annual incidence representing 10% of new lymphomas and a prevalence of 1% of all cancers in Western countries [1]. Patient outcomes are mostly excellent with multiagent chemotherapy (escalated BEACOPP, ABVD) and modern radiation techniques with a five-year progression-free survival (PFS) rate of 65% to 90% according to advanced versus localized stage disease and standard clinical risk factors [2][3]. Patients achieving an early metabolic rapid response after two cycles of frontline chemotherapy display excellent outcome and are proposed for treatment intensity de-escalation after interim positron emission tomography (PET) assessment [4]. However, approximately one-quarter of patients will display progressive disease or relapse, pinpointing the urgent need to determine the underlying biological processes involved and to select useful biomarkers. At present, no pathognomonic biomarker exists for cHL. To date, the genetic landscape of cHL has been incompletely described because Hodgkin and Reed-Sternberg (HRS) tumor cells are very scarce (0.1–3% of cells in the tissue) [5], hampering molecular biology analyses with techniques that lack sensitivity.

Standard lymph node removal or tissue biopsy is the recommended procedure for the lymphoma diagnosis and is the routine method for tumor genetic profiling, but this invasive method is associated with numerous caveats: hemorrhage, infections, anesthetic risks, or poor-quality fine-needle biopsy with artifacts or sampling issues [6]. In addition, the rarity of HRS cells augments pathological diagnosis complexity in small biopsy samples. In line with these tumor DNA access difficulties, several teams hypothesized that discovering tumor specific somatic alterations would be more appropriate in the bloodstream, i.e., in cell-free DNA (cfDNA) extracted from plasma. Indeed, various proof-of-concept studies have recently shown that cHL molecular analysis is feasible using cfDNA and highly sensitive methods [7][8][9][10][11]. Despite the scarcity of HRS cells and a typical lower tumor volume, many studies have indicated that success rates for cfDNA detection in cHL are close to those achieved for diffuse large B cell lymphoma (DLBCL). This finding suggests that cHL seems to display a higher trend to release cfDNA than DLBCL. This finding may be related to an increased fraction of tumor cells in apoptosis but also related to the nature of the alterations in the nuclear DNA of reed Sternberg cells [12].

1.2. Cell-Free DNA Physiopathology

cfDNA was discovered in the bloodstream several years ago and was first assessed in solid tumors. Previous interesting works in solid tumors settings established that cfDNA fragments may invade distant cells in other tissues, modifying the biology of these cells and contributing to the onset of metastases [13][14]. For cHL, no similar data exist. cfDNA is predominantly freed in plasma by cells in apoptosis and is in addition actively secreted by certain tumor cells or liberated during necrosis phenomenon (see Figure 1). These facts are established by the existing broad spectrum of cfDNA fragment sizes (from 0.150 to several kilobases) and the demonstration of similar genetic profiles in tumor genomic DNA (gDNA) and cfDNA [15][16][17]. Of note, cfDNA exist in healthy subjects and mainly derived from apoptosis of hematopoietic cells. This normal cfDNA is typically detectable in very small quantities in blood [18]. However, these levels increase 15-fold 30 min postexercise and return to normal levels thereafter [19]. The physiological role of cfDNA remains undoubtedly elusive, but a twofold increase in cfDNA levels after the psychosocial stress test and five-fold increase after exhaustive treadmill exercise were recently demonstrated, suggesting that cfDNA is a biomarker of molecular stress [20]. Biological and environmental variables may also modulate cfDNA release, including sex, body composition, age, smoking, exercise, autoimmune disorders, comorbidities, infectious disease, inflammatory conditions, oxidative stress, and pregnancy [21][22]. In addition, cfDNA clearance is also complex and may imply DNase I activity [23], renal clearance [24], and uptake by the liver and spleen followed by macrophage elimination [25]. Several teams measured that the cfDNA half-life in blood is comprised in a range of 16 min to 2.5 h [26] but is largely dependent on several patient settings: healthy subjects vs. cancer patients, before surgery/chemotherapy/radiotherapy or after, and at rest vs. after physical activity [22]. Furthermore, cfDNA clearance is also influenced by binding to cell-surface receptors [27] and several serum proteins [28] (albumin, fibrinogen, prothrombin, and C-reactive protein), the levels of which may considerably vary during the cancer course. All these features complicate biological studies on this topic. It is important to note that cfDNA combined both “normal/non-tumoral” cfDNA and circulating tumor DNA (ctDNA) fragments [18][29], and there is no tool to separate cfDNA arisen from cancer cells and cfDNA liberated by normal cells, which begs the question of background noise and sensitivity of the technologies used in liquid biopsy works to detect somatic variants. Of note, several research teams in solid tumors detected cfDNA in other human fluids, such as cerebrospinal fluid (CSF) in primary central nervous system lymphomas (PCNSLs) with MYD88 L265P mutation assessment [30][31], urine [32] in the bladder cancer, sputum [33] in lung cancer patients, and uterine lavage fluid in patients with endometrial cancers [34]. In addition, stool DNA may also be valuable in patients with colorectal carcinoma with improved detection rates and a commercially available tool (Cologuard™ assay) [35]. However, these sources of cfDNA seems irrelevant for cHL with no published data to date and no possibility to determine their value for disease burden assessment or genotyping at the time of diagnosis.

Pharmaceuticals 14 00207 g001 550

Figure 1. Schematic overview of cell-free DNA in classical Hodgkin lymphoma. Abbreviations: MRD: minimal residual disease.

Finally, relevant advantages of plasma cfDNA testing in cHL include: (i) simple venous puncture to obtain sample, (ii) measurable tool which may be performed at any time during patient’s journey (iii) dynamic assessment of clonal evolution, and (iv) less spatial heterogeneity than tissue biopsy genotyping [36][37][38].

1.3. Cell-Free DNA Molecular Tools

We know that the optimal way for noninvasive liquid biopsy testing is to extract cfDNA from plasma after blood puncture with nucleic acid preservation tubes (for example, Roche or Streck cfDNA BCT ® [39]). These tubes should then be promptly processed (within 6 h after venous puncture) with consecutive low- and high-speed [40] centrifugations to reduce leukocyte lysis. The intensity of low temperature storage room (−20 °C or −80 °C) remains controversial, but the relevance of leukocyte stabilization tubes is clearly established for easier use [41], especially in multicentric studies. Research teams may experience altered sample quality if they do not satisfy the optimal preanalytical requirements [42]. cfDNA extraction from plasma samples is easy and feasible in most academic laboratories using commercial kits [43][44]. As mentioned above, in cHL, cfDNA comes from rare lymphoma cells and normal cells, thereby necessitating highly sensitive methods for accurate measurement of somatic alterations.

It was previously established that tumor cells in cHL arise from B-cells [45], and clono-specific B-cells are detectable in cHL patients’ blood samples [46]. Normal C-cells and lymphoma cells both expressed B-cell receptor (BCR). BCR variety is a consequence of variable-diversity-joining (VDJ) genes rearrangement during lymphopoiesis. This mechanism provides specific clonotypes, and so each tumor-specific VDJ profile may be considered as a “barcode” for noninvasive tracking of lymphoma in liquid biopsy. Indeed, using ClonoSEQ technology (Adaptive Biotechnologies, Seattle, WA, USA) Oki et al. described a small proof of concept series of seventeen patients, of whom eleven harbored a detectable lymphoma-specific clonotype in tumor biopsies, 8/11 (73%) displayed the same clonotypes in plasma cfDNA, and 33% exhibited the same clonotypes in PBMCs [47]. Using universal VDJ and IdK primers instead of tumor-specific primers, it is possible to detect clonospecific sequences in a single cfDNA sample. This may grant to disclose exhaustive patients’ immunoglobulin repertoire and monitor individual subclones. This ClonoSEQ assay is the sole FDA-cleared minimal residual disease (MRD) tool in lymphomas. Nevertheless, to our knowledge, ClonoSEQ technology results in cHL patients have not been reproduced by other teams, and the sensitivity and specificity of this technique in cHL remain unclear at the moment. Moreover, this technique requires the initial tumor biopsy material for exact assessment of the VDJ profile before tracking it in the blood. In addition, VDJ rearrangements may be unproductive or abortive [48], so the ClonoSEQ method may not work for in this situation, restricting the informativeness of this tool.

In contrast, next-generation sequencing (NGS) gene panel tests may detect concordant potentially “actionable” somatic mutations in the patients’ plasma (cfDNA) and biopsy (gDNA) of lymphoma patients [49] and may contribute to decide appropriate salvage treatments in relapsed/refractory aggressive B-cell lymphoma using new target therapies currently in development. For example, CAncer Personalized Profiling by deep Sequencing (CAPP-seq) is a powerful method for cfDNA measurement that allows deep DNA sequencing and grants an easy detection and quantification of ultralow abundance genetic alterations [50]. CAPP-seq relevance was well described in non-Hodgkin and Hodgkin lymphoma patients. This technology is able to measure disease burden, detect early relapse before radiological progression, perform cell of origin (COO) classification, separate indolent follicular lymphomas and those at risk for high grade transformation, and monitor variants’ clearance in chemosensitive patients versus non-responders patients who display persistent genetic alterations in plasma after treatment [8][51][52][53]. However, such results are only possible at high cost given the elevated number of genes included in the panels and in trained research teams with experienced bioinformaticians able to combine barcoding and unique molecular identifiers (UMIs) with integrated digital error suppression [54]. To date, CAPP-seq is not commercially available. Another report recently assessed cfDNA using real-time PCR in an impressive cohort of 155 pediatric cHL. In this work, the authors showed that baseline cfDNA level is higher in cHL than in healthy subjects, and that higher cfDNA concentration is linked to B-symptoms and inflammatory syndrome. The authors also established that the augmentation of cfDNA concentration after one cycle of chemotherapy led to unfavorable outcome [55].

Finally, digital PCR (dPCR) is a quick, simple and barely costless tool which only needs a small amount of plasma cfDNA. dPCR process dilutes and partitions DNA samples into thousands microcompartments (i.e., microscopic PCR reactors) with each one including a single copy or no copies of the target region [56]. It is then easy to quantify the exact normal or mutated DNA copies number by counting the number of positive compartments with the fluorescent probe corresponding to the wild-type or mutated region. The advantages of dPCR include rapid implementation, the lack of a need for a bioinformatics pipeline and high sensitivity (10−5 detection limit), making it a relevant tool when used independently and in addition to NGS for hotspot single-nucleotide variant (SNV) detection, such as XPO1 E571K (primary mediastinal B-cell lymphoma and cHL) [44]. Indeed, dPCR is based on single-point mutations quantification, and so is not designed to provide the complete molecular landscape of the patient’s lymphoma and, therefore, probably not suitable for molecular response assessment given frequent subclonal evolution. In addition, treatment sensitivity could remove those subclones from cfDNA profiles, so other clones could arise or survive with a false negative dPCR assay. A solution could be to multiplexe dPCR assays to test several hotspot mutations in the same experience, and indeed new dPCR tools can do so (including RainDance® or Biorad®) [57][58][59][60]. Furthermore, low amount of cfDNA in some plasma samples impairs the capability to measure molecular response given insufficient haploid genome equivalent quantities. Of note, false positives/background noise issues and technical limits of variant detection are still debated [61][62]. In addition, to date, no published multicenter study of dPCR MRD approaches exists in cHL patients.

2. Genotyping Classical Hodgkin Lymphoma Using cfDNA

2.1. Mutational Landscape Obtained by cfDNA Sequencing

A notable NGS study with low-pass sequencing on cfDNA of ten newly-diagnosed localized and advanced stage nodular sclerosis cHL patients revealed genomic imbalances in HRS cells in nine patients at baseline and a rapid clearance after frontline treatment (within a month), revealing cfDNA as a promising tool for molecular response monitoring [11]. In a retrospective proof-of-concept study from our group, including 94 patients with all stages of cHL homogeneously treated with standard frontline chemotherapy, XPO1 E571K mutations were found using dPCR and NGS experiments in 24.2% of patients. We noted that 29% of all XPO1 E571K mutations were only discovered in cfDNA, which may be explained by HRS cell scarcity in cHL. Our group was then able to develop a multigene panel allowing the detection of several somatic alterations of genes involved in the lymphomagenesis of cHL or frequently mutated in this disease. By dPCR and NGS, we found an average of 2.13 mutations per case of cHL; in particular, 30.5% of patients were mutated in the DNA binding domain of STAT6 [9]. However, this panel was only informative for 50% of the patients using cfDNA sources, so we extended it to a nine-gene panel including SOCS1, XPO1, STAT6, NFKBIE, TNFAIP3, PTPN1, B2M, ITPKB, and GNA13. Our team also led an observational prospective study based on cfDNA testing including 60 consecutive cHL cases treated by frontline ABVD and/or escalated BEACOPP. We observed somatic variants in 42/60 (70%) patients at baseline [10]. However, this gene panel was unable to disclose variants in all of the patients, probably because the panel was too restricted and sensitivity was insufficient to reveal ultra-low abundance subclones which are close to the sequencer limit of detection (variant allele frequency (VAF) 0.1%). The comprehension of cHL biology is growing quickly, and we should include additional genes in next panels. For example, ATM, KMT2D, TP53, ARID1A, and CIITA are interesting and frequently mutated in cHL.

In addition, in 2018, Spina et al. reported a major study establishing the genetic panorama of cHL patients using CAPP-seq on plasma-extracted cfDNA. The most commonly mutated genes encompassed STAT6 (37.5%), TNFAIP3 (35%), ITPKB (27.5%), GNA13 (18.7%), B2M (16.2%), ATM (15%), SPEN (12.5%), and XPO1 (11.2%) [8]. The predominance of STAT6 alterations is a discovery that was never reported in past exome works [63] and clearly reflects the impact of cytokines signaling pathway in cHL [64].

2.2. Comparisons between cfDNA and Tumor DNA

In solid tumors [59][60], the similarity rate between variants found in paired tumors and cfDNA samples changed from 88.2% to 64.7% for time intervals of less than three weeks and >3 weeks between venous puncture and tissue biopsy, respectively [60], revealing a sampling time issue. Thompson et al. also demonstrated in lung cancer that increasing the time between tumor and blood collection from < 14 days to >6 months highly reduced the similarity rate [61].

Using CAPP-seq with cfDNA, microdissected HRS-cell enriched areas from biopsies, paired tumor genomic DNA (gDNA) and paired normal gDNA, Spina et al. demonstrated the tumor origin of cfDNA variants depicted in their cHL patients. The similarity (R2 = 0.978) of mutational profiles from paired gDNA/cfDNA samples favors the capability of CAPP-seq to precisely detect low burden variants in cfDNA. In our experience, comparability between gDNA and cfDNA profiles with an NGS-limited gene panel is close to 85% [10] at the level variant. Of note, median VAF appears to be higher in cfDNA than in biopsies probable due to the common scarcity of tumor cells in cHL biopsies [10]. In the study by Desch et al. [65] in pediatric cHL patients, the average VAFs were 1.1% for tumor DNA (from whole tissue sections) and 11.1% for cfDNA, but all 30 variants discovered in cfDNA were then confirmed in macrodissected HRS-cell rich regions of paired tumor biopsies, confirming the reliability of cfDNA-obtained mutational profiles. Of note, fresh frozen tissue led to a better concordance rate between genomic DNA and cfDNA (57.1% vs. 66.7% for FFPE tissue) given DNA alterations induced by the FFPE process, particularly for amplicon-based amplification assays [66][67][68]. Nevertheless, this issue could be largely fixed by FFPE DNA repair methods [69] before NGS sequencing. Notwithstanding, tumor subclones are probably dynamically dispersed between various anatomical sites (spatial heterogeneity), which may prevent exhaustive discovery of all possibly existing variants mutations in a unique lymph node resection or fine-needle biopsy. In our opinion, this issue may be surmounted by assessing paired tumor biopsy/plasma cfDNA samples. It now seems established that cfDNA is an excellent mirror of the HRS cell genetic panorama (see Figure 1).

2.3. Comparisons between cfDNA Results and cHL Histological Subtypes

In the WHO classification, four distinct cHL subtypes are described [70]: Nodular sclerosis cHL (NSCHL), which is the most common, mixed cellularity (MCCHL), lymphocyte-depleted cHL (LDCHL), and lymphocyte-rich cHL (LRCHL) [71]. Clinical characteristics, overall prognosis, HRS cells phenotype and treatments are similar but the transcriptome and microenvironment show substantial differences. HRS cells typically express MYC, NOTCH1, and IRF4 in all cHL histologic subtypes [72][73].

Nevertheless, gene expression profiling studies demonstrated at the transcriptome level [74][75] that the histologic subtypes of cHL are also biologically distinct. According to Reichel et al., B2M mutations are exclusively found in the nodular sclerosis subtype [63]. The work published by Spina et al. [8] confirms these data and indicates that these subtypes are distinct at the genetic level. In particular, NSCHL and EBER-negative cHL are associated with more frequent STAT6 and TNFAIP3 cfDNA somatic mutations than other subtypes. Nevertheless, XPO1 E571K recurrent mutations are detectable in all subtypes and so are not a pathognomonic feature of a particular subtype [7]. Plasma cfDNA concentrations at baseline and genetic profiles from tumor biopsies at diagnosis were also assessed in the 4 cHL subtypes, and no differences were observed in another retrospective study [9].

2.4. Potential Interest in the Differential Diagnosis with Other Lymphomas (Gray-Zone, Primary Mediastinal B Cell Lymphoma)

cHL is sometimes hard to diagnose and can therefore be mistaken for several differential diagnoses, including DLBCL, primary mediastinal large B-cell lymphoma (PMBL), anaplastic large cell lymphoma (ALCL), and mediastinal gray-zone lymphoma (MGZL), both of which may display CD30 positivity [70]. In particular, PMBL and NSCHL pathological features are overlapping, so several authors estimated that these two entities are derived from thymic B cells [76][77]. The data demonstrating that the XPO1 E571K, STAT6, and SOCS1 mutations are frequent PMBL and NSHL but rare in DLBCL [7][8][10][78] support the idea of a shared oncogenic origin between cHL and PMBL. In our opinion, we may use XPO1 E571K detection by cfDNA analysis to help pathologists to orient between NSCHL, MGZL and DLBCL, especially in the relapse setting if this variant was already present at diagnosis, despite the lack of specificity of this hotspot mutation [78][79][80]. In addition, STAT6 mutations are easily detectable by cfDNA analysis, are not observed in nodular lymphocyte predominant Hodgkin lymphoma [81] (NLPHL) and are frequent in cHL [8]. Thus, STAT6 mutations may be useful to differentially diagnose these two entities.

References

  1. Diehl, V.; Thomas, R.K.; Re, D. Part II: Hodgkin’s lymphoma--diagnosis and treatment. Lancet Oncol. 2004, 5, 19–26.
  2. Hasenclever, D.; Diehl, V. A prognostic score for advanced Hodgkin’s disease. International Prognostic Factors Project on Advanced Hodgkin’s Disease. N. Engl. J. Med. 1998, 339, 1506–1514.
  3. Casasnovas, R.-O.; Bouabdallah, R.; Brice, P.; Lazarovici, J.; Ghesquieres, H.; Stamatoullas, A.; Dupuis, J.; Gac, A.-C.; Gastinne, T.; Joly, B.; et al. PET-Adapted Treatment for Newly Diagnosed Advanced Hodgkin Lymphoma (AHL2011): A Randomised, Multicentre, Non-Inferiority, Phase 3 Study. Lancet Oncol. 2019, 20, 202–215.
  4. Hutchings, M.; Loft, A.; Hansen, M.; Pedersen, L.M.; Buhl, T.; Jurlander, J.; Buus, S.; Keiding, S.; D’Amore, F.; Boesen, A.-M.; et al. FDG-PET after Two Cycles of Chemotherapy Predicts Treatment Failure and Progression-Free Survival in Hodgkin Lymphoma. Blood 2006, 107, 52–59.
  5. Schmitz, R.; Stanelle, J.; Hansmann, M.-L.; Küppers, R. Pathogenesis of classical and lymphocyte-predominant Hodgkin lymphoma. Annu. Rev. Pathol. 2009, 4, 151–174.
  6. VanderLaan, P.A. Fine-needle aspiration and core needle biopsy: An update on 2 common minimally invasive tissue sampling modalities: FNA Versus CNB. Cancer Cytopathol. 2016, 124, 862–870.
  7. Camus, V.; Stamatoullas, A.; Mareschal, S.; Viailly, P.-J.; Sarafan-Vasseur, N.; Bohers, E.; Dubois, S.; Picquenot, J.M.; Ruminy, P.; Maingonnat, C.; et al. Detection and Prognostic Value of Recurrent Exportin 1 Mutations in Tumor and Cell-Free Circulating DNA of Patients with Classical Hodgkin Lymphoma. Haematologica 2016, 101, 1094–1101.
  8. Spina, V.; Bruscaggin, A.; Cuccaro, A.; Martini, M.; Di Trani, M.; Forestieri, G.; Manzoni, M.; Condoluci, A.; Arribas, A.; Terzi-Di-Bergamo, L.; et al. Circulating Tumor DNA Reveals Genetics, Clonal Evolution, and Residual Disease in Classical Hodgkin Lymphoma. Blood 2018, 131, 2413–2425.
  9. Bessi, L.; Viailly, P.-J.; Bohers, E.; Ruminy, P.; Maingonnat, C.; Bertrand, P.; Vasseur, N.; Beaussire, L.; Cornic, M.; Etancelin, P.; et al. Somatic Mutations of Cell-Free Circulating DNA Detected by Targeted next-Generation Sequencing and Digital Droplet PCR in Classical Hodgkin Lymphoma. Leuk. Lymphoma 2018, 60, 498–502.
  10. Camus, V.; Viennot, M.; Lequesne, J.; Viailly, P.-J.; Bohers, E.; Bessi, L.; Marcq, B.; Etancelin, P.; Dubois, S.; Picquenot, J.-M.; et al. Targeted Genotyping of Circulating Tumor DNA for Classical Hodgkin Lymphoma Monitoring: A Prospective Study. Haematologica 2020, 106, 154–162.
  11. Vandenberghe, P.; Wlodarska, I.; Tousseyn, T.; Dehaspe, L.; Dierickx, D.; Verheecke, M.; Uyttebroeck, A.; Bechter, O.; Delforge, M.; Vandecaveye, V.; et al. Non-Invasive Detection of Genomic Imbalances in Hodgkin/Reed-Sternberg Cells in Early and Advanced Stage Hodgkin’s Lymphoma by Sequencing of Circulating Cell-Free DNA: A Technical Proof-of-Principle Study. Lancet Haematol. 2015, 2, e55–e65.
  12. Righolt, C.H.; Knecht, H.; Mai, S. DNA Superresolution Structure of Reed-Sternberg Cells Differs Between Long-Lasting Remission Versus Relapsing Hodgkin’s Lymphoma Patients: DNA Structure In Pre-Treatment Hodgkin’s Lymphoma. J. Cell Biochem. 2016, 117, 1633–1637.
  13. Wen, F.; Shen, A.; Choi, A.; Gerner, E.W.; Shi, J. Extracellular DNA in Pancreatic Cancer Promotes Cell Invasion and Metastasis. Cancer Res. 2013, 73, 4256–4266.
  14. Bendich, A.; Wilczok, T.; Borenfreund, E. Circulating DNA as a Possible Factor in Oncogenesis. Science 1965, 148, 374–376.
  15. Diaz, L.A.; Bardelli, A. Liquid biopsies: Genotyping circulating tumor DNA. J. Clin. Oncol. 2014, 32, 579–586.
  16. Lowes, L.E.; Bratman, S.V.; Dittamore, R.; Done, S.; Kelley, S.O.; Mai, S.; Morin, R.D.; Wyatt, A.W.; Allan, A.L. Circulating Tumor Cells (CTC) and Cell-Free DNA (CfDNA) Workshop 2016: Scientific Opportunities and Logistics for Cancer Clinical Trial Incorporation. Int. J. Mol. Sci. 2016, 17, 1505.
  17. Alizadeh, A.A.; Aranda, V.; Bardelli, A.; Blanpain, C.; Bock, C.; Borowski, C.; Caldas, C.; Califano, A.; Doherty, M.; Elsner, M.; et al. Toward Understanding and Exploiting Tumor Heterogeneity. Nat. Med. 2015, 21, 846–853.
  18. Stroun, M.; Lyautey, J.; Lederrey, C.; Olson-Sand, A.; Anker, P. About the possible origin and mechanism of circulating DNA apoptosis and active DNA release. Clin. Chim. Acta Int. J. Clin. Chem. 2001, 313, 139–142.
  19. Fatouros, I.G.; Jamurtas, A.Z.; Nikolaidis, M.G.; Destouni, A.; Michailidis, Y.; Vrettou, C.; Douroudos, I.I.; Avloniti, A.; Chatzinikolaou, A.; Taxildaris, K.; et al. Time of Sampling Is Crucial for Measurement of Cell-Free Plasma DNA Following Acute Aseptic Inflammation Induced by Exercise. Clin. Biochem. 2010, 43, 1368–1370.
  20. Hummel, E.M.; Hessas, E.; Müller, S.; Beiter, T.; Fisch, M.; Eibl, A.; Wolf, O.T.; Giebel, B.; Platen, P.; Kumsta, R.; et al. Cell-Free DNA Release under Psychosocial and Physical Stress Conditions. Transl. Psychiatry 2018, 8, 236.
  21. Van der Vaart, M.; Pretorius, P.J. The Origin of Circulating Free DNA. Clin. Chem. 2007, 53, 2215.
  22. Bronkhorst, A.J.; Ungerer, V.; Holdenrieder, S. The emerging role of cell-free DNA as a molecular marker for cancer management. BioMol. Detect. Quantif. 2019, 17, 100087.
  23. Tamkovich, S.N.; Cherepanova, A.V.; Kolesnikova, E.V.; Rykova, E.Y.; Pyshnyi, D.V.; Vlassov, V.V.; Laktionov, P.P. Circulating DNA and DNase Activity in Human Blood. Ann. N. Y. Acad. Sci. 2006, 1075, 191–196.
  24. Botezatu, I.; Serdyuk, O.; Potapova, G.; Shelepov, V.; Alechina, R.; Molyaka, Y.; Ananév, V.; Bazin, I.; Garin, A.; Narimanov, M.; et al. Genetic Analysis of DNA Excreted in Urine: A New Approach for Detecting Specific Genomic DNA Sequences from Cells Dying in an Organism. Clin. Chem. 2000, 46, 1078–1084.
  25. Diehl, F.; Li, M.; Dressman, D.; He, Y.; Shen, D.; Szabo, S.; Diaz, L.A.; Goodman, S.N.; David, K.A.; Juhl, H.; et al. Detection and Quantification of Mutations in the Plasma of Patients with Colorectal Tumors. Proc. Natl. Acad. Sci. 2005, 102, 16368–16373.
  26. Yao, W.; Mei, C.; Nan, X.; Hui, L. Evaluation and comparison of in vitro degradation kinetics of DNA in serum, urine and saliva: A qualitative study. Gene 2016, 590, 142–148.
  27. Chelobanov, B.P.; Laktionov, P.P.; Vlasov, V.V. Proteins involved in binding and cellular uptake of nucleic acids. Biochem. Mosc. 2006, 71, 583–596.
  28. Thierry, A.R.; El Messaoudi, S.; Gahan, P.B.; Anker, P.; Stroun, M. Origins, structures, and functions of circulating DNA in oncology. Cancer Metastasis Rev. 2016, 35, 347–376.
  29. Jahr, S.; Hentze, H.; Englisch, S.; Hardt, D.; Fackelmayer, F.O.; Hesch, R.D.; Knippers, R. DNA Fragments in the Blood Plasma of Cancer Patients: Quantitations and Evidence for Their Origin from Apoptotic and Necrotic Cells. Cancer Res. 2001, 61, 1659–1665.
  30. Fontanilles, M.; Marguet, F.; Bohers, E. Somatic Mutations Detected in Plasma Cell-Free DNA By Targeted Sequencing: Assessment of Liquid Biopsy in Primary Central Nervous System Lymphoma. Blood 2015, 126, 332.
  31. Rimelen, V.; Ahle, G.; Pencreach, E.; Zinniger, N.; Debliquis, A.; Zalmaï, L.; Harzallah, I.; Hurstel, R.; Alamome, I.; Lamy, F.; et al. Tumor Cell-Free DNA Detection in CSF for Primary CNS Lymphoma Diagnosis. Acta Neuropathol. Commun. 2019, 7, 43.
  32. Streleckiene, G.; Reid, H.M.; Arnold, N.; Bauerschlag, D.; Forster, M. Quantifying cell free DNA in urine: Comparison between commercial kits, impact of gender and inter-individual variation. BioTechniques 2018, 64, 225–230.
  33. Fumagalli, C.; Bianchi, F.; Rafaniello Raviele, P.; Vacirca, D.; Bertalot, G.; Rampinelli, C.; Lazzeroni, M.; Bonnani, B.; Veronesi, G.; Fusco, N.; et al. Circulating and Tissue Biomarkers in Early-Stage Non-Small. Ecancermedicalscience 2017, 11, 717.
  34. Nair, N.; Camacho-Vanegas, O.; Rykunov, D.; Dashkoff, M.; Camacho, S.C.; Schumacher, C.A.; Irish, J.C.; Harkins, T.T.; Freeman, E.; Garcia, I.; et al. Genomic Analysis of Uterine Lavage Fluid Detects Early Endometrial Cancers and Reveals a Prevalent Landscape of Driver Mutations in Women without Histopathologic Evidence of Cancer: A Prospective Cross-Sectional Study. PLoS Med. 2016, 13, e1002206.
  35. Imperiale, T.F.; Ransohoff, D.F.; Itzkowitz, S.H.; Levin, T.R.; Lavin, P.; Lidgard, G.P.; Ahlquist, D.A.; Berger, B.M. Multitarget Stool DNA Testing for Colorectal-Cancer Screening. N. Engl. J. Med. 2014, 370, 1287–1297.
  36. Chae, Y.K.; Davis, A.A.; Carneiro, B.A.; Chandra, S.; Mohindra, N.; Kalyan, A.; Kaplan, J.; Matsangou, M.; Pai, S.; Costa, R.; et al. Concordance between Genomic Alterations Assessed by Next-Generation Sequencing in Tumor Tissue or Circulating Cell-Free DNA. Oncotarget 2016, 7, 65364.
  37. Huang, A.; Zhang, X.; Zhou, S.-L.; Cao, Y.; Huang, X.-W.; Fan, J.; Yang, X.-R.; Zhou, J. Detecting Circulating Tumor DNA in Hepatocellular Carcinoma Patients Using Droplet Digital PCR Is Feasible and Reflects Intratumoral Heterogeneity. J. Cancer 2016, 7, 1907–1914.
  38. Liao, W.; Yang, H.; Xu, H.; Wang, Y.; Ge, P.; Ren, J.; Xu, W.; Lu, X.; Sang, X.; Zhong, S.; et al. Noninvasive Detection of Tumor-Associated Mutations from Circulating Cell-Free DNA in Hepatocellular Carcinoma Patients by Targeted Deep Sequencing. Oncotarget 2016, 7, 40481–40490.
  39. Parackal, S.; Zou, D.; Day, R.; Black, M.; Guilford, P. Comparison of Roche Cell-Free DNA collection Tubes to Streck Cell-Free DNA BCT s for sample stability using healthy volunteers. Pract. Lab. Med. 2019, 16, e00125.
  40. Merker, J.D.; Oxnard, G.R.; Compton, C.; Diehn, M.; Hurley, P.; Lazar, A.J.; Lindeman, N.; Lockwood, C.M.; Rai, A.J.; Schilsky, R.L.; et al. Circulating Tumor DNA Analysis in Patients With Cancer: American Society of Clinical Oncology and College of American Pathologists Joint Review. Arch. Pathol. Lab. Med. 2018, 142, 1242–1253.
  41. Kang, Q.; Henry, N.L.; Paoletti, C.; Jiang, H.; Vats, P.; Chinnaiyan, A.M.; Hayes, D.F.; Merajver, S.D.; Rae, J.M.; Tewari, M. Comparative Analysis of Circulating Tumor DNA Stability In K3EDTA, Streck, and CellSave Blood Collection Tubes. Clin. Biochem. 2016, 49, 1354–1360.
  42. El Messaoudi, S.; Rolet, F.; Mouliere, F.; Thierry, A.R. Circulating cell free DNA: Preanalytical considerations. Clin. Chim. Acta Int. J. Clin. Chem. 2013, 424, 222–230.
  43. Murtaza, M.; Dawson, S.-J.; Tsui, D.W.Y.; Gale, D.; Forshew, T.; Piskorz, A.M.; Parkinson, C.; Chin, S.-F.; Kingsbury, Z.; Wong, A.S.C.; et al. Non-Invasive Analysis of Acquired Resistance to Cancer Therapy by Sequencing of Plasma DNA. Nature 2013, 497, 108–112.
  44. Camus, V.; Sarafan-Vasseur, N.; Bohers, E.; Dubois, S.; Mareschal, S.; Bertrand, P.; Viailly, P.-J.; Ruminy, P.; Maingonnat, C.; Lemasle, E.; et al. Digital PCR for Quantification of Recurrent and Potentially Actionable Somatic Mutations in Circulating Free DNA from Patients with Diffuse Large B-Cell Lymphoma. Leuk. Lymphoma 2016, 57, 2171–2179.
  45. Küppers, R.; Rajewsky, K.; Zhao, M.; Simons, G.; Laumann, R.; Fischer, R.; Hansmann, M.L. Hodgkin Disease: Hodgkin and Reed-Sternberg Cells Picked from Histological Sections Show Clonal Immunoglobulin Gene Rearrangements and Appear to Be Derived from B Cells at Various Stages of Development. Proc. Natl. Acad. Sci. USA 1994, 91, 10962–10966.
  46. Jones, R.J.; Gocke, C.D.; Kasamon, Y.L.; Miller, C.B.; Perkins, B.; Barber, J.P.; Vala, M.S.; Gerber, J.M.; Gellert, L.L.; Siedner, M.; et al. Circulating Clonotypic B Cells in Classic Hodgkin Lymphoma. Blood 2009, 113, 5920–5926.
  47. Oki, Y.; Neelapu, S.S.; Fanale, M.; Kwak, L.W.; Fayad, L.; Rodriguez, M.A.; Wallace, M.; Klinger, M.; Carlton, V.; Kong, K.; et al. Detection of Classical Hodgkin Lymphoma Specific Sequence in Peripheral Blood Using a Next-Generation Sequencing Approach. Br. J. Haematol. 2015, 169, 689–693.
  48. Daly, J.; Licence, S.; Nanou, A.; Morgan, G.; Mårtensson, I.-L. Transcription of productive and nonproductive VDJ-recombined alleles after IgH allelic exclusion. EMBO J. 2007, 26, 4273–4282.
  49. Bohers, E.; Viailly, P.J.; Dubois, S.; Bertrand, P.; Maingonnat, C.; Mareschal, S.; Ruminy, P.; Picquenot, J.-M.; Bastard, C.; Desmots, F.; et al. Somatic Mutations of Cell-Free Circulating DNA Detected by next-Generation Sequencing Reflect the Genetic Changes in Both Germinal Center B-Cell-like and Activated B-Cell-like Diffuse Large B-Cell Lymphomas at the Time of Diagnosis. Haematologica 2015, 100, e280–e284.
  50. Newman, A.M.; Bratman, S.V.; To, J.; Wynne, J.F.; Eclov, N.C.W.; Modlin, L.A.; Liu, C.L.; Neal, J.W.; Wakelee, H.A.; Merritt, R.E.; et al. An Ultrasensitive Method for Quantitating Circulating Tumor DNA with Broad Patient Coverage. Nat. Med. 2014, 20, 548–554.
  51. Kurtz, D.M.; Scherer, F.; Newman, A.M.; Lovejoy, A.F.; Klass, D.M.; Chabon, J.J.; Gambhir, S.; Diehn, M.; Alizadeh, A.A. Dynamic Noninvasive Genomic Monitoring for Outcome Prediction in Diffuse Large B-Cell Lymphoma. Blood 2015, 126, 130.
  52. Rossi, D.; Diop, F.; Spaccarotella, E.; Monti, S.; Zanni, M.; Rasi, S.; Deambrogi, C.; Spina, V.; Bruscaggin, A.; Favini, C.; et al. Diffuse Large B-Cell Lymphoma Genotyping on the Liquid Biopsy. Blood 2017, 129, 1947–1957.
  53. Scherer, F.; Kurtz, D.M.; Newman, A.M.; Stehr, H.; Craig, A.F.M.; Esfahani, M.S.; Lovejoy, A.F.; Chabon, J.J.; Klass, D.M.; Liu, C.L.; et al. Distinct Biological Subtypes and Patterns of Genome Evolution in Lymphoma Revealed by Circulating Tumor DNA. Sci. Transl. Med. 2016, 8, 364ra155.
  54. Sater, V.; Viailly, P.-J.; Lecroq, T.; Prieur-Gaston, É.; Bohers, É.; Viennot, M.; Ruminy, P.; Dauchel, H.; Vera, P.; Jardin, F. UMI-VarCal: A New UMI-Based Variant Caller That Efficiently Improves Low-Frequency Variant Detection in Paired-End Sequencing NGS Libraries. Bioinformatics 2020, 36, 2718–2724.
  55. Primerano, S.; Burnelli, R.; Carraro, E.; Pillon, M.; Elia, C.; Farruggia, P.; Sala, A.; Vinti, L.; Buffardi, S.; Basso, G.; et al. Kinetics of Circulating Plasma Cell-Free DNA in Paediatric Classical Hodgkin Lymphoma. J. Cancer 2016, 7, 364–366.
  56. Baker, M. Digital PCR hits its stride. Nat. Methods 2012, 9, 541–544.
  57. Schiavon, G.; Hrebien, S.; Garcia-Murillas, I.; Cutts, R.J.; Pearson, A.; Tarazona, N.; Fenwick, K.; Kozarewa, I.; Lopez-Knowles, E.; Ribas, R.; et al. Analysis of ESR1 Mutation in Circulating Tumor DNA Demonstrates Evolution during Therapy for Metastatic Breast Cancer. Sci. Transl. Med. 2015, 7, 313ra182.
  58. Taly, V.; Pekin, D.; Benhaim, L.; Kotsopoulos, S.K.; Le Corre, D.; Li, X.; Atochin, I.; Link, D.R.; Griffiths, A.D.; Pallier, K.; et al. Multiplex Picodroplet Digital PCR to Detect KRAS Mutations in Circulating DNA from the Plasma of Colorectal Cancer Patients. Clin. Chem. 2013, 59, 1722–1731.
  59. Tone, A.A.; McConechy, M.K.; Yang, W.; Ding, J.; Yip, S.; Kong, E.; Wong, K.-K.; Gershenson, D.M.; Mackay, H.; Shah, S.; et al. Intratumoral Heterogeneity in a Minority of Ovarian Low-Grade Serous Carcinomas. BMC Cancer 2014, 14, 982.
  60. Laurent-Puig, P.; Pekin, D.; Normand, C.; Kotsopoulos, S.K.; Nizard, P.; Perez-Toralla, K.; Rowell, R.; Olson, J.; Srinivasan, P.; Le Corre, D.; et al. Clinical Relevance of KRAS-Mutated Subclones Detected with Picodroplet Digital PCR in Advanced Colorectal Cancer Treated with Anti-EGFR Therapy. Clin. Cancer Res. Off. J. Am. Assoc. Cancer Res. 2015, 21, 1087–1097.
  61. Dugo, N.; Padula, F.; Mobili, L.; Brizzi, C.; D’Emidio, L.; Cignini, P.; Mesoraca, A.; Bizzoco, D.; Cima, A.; Giorlandino, C. Six Consecutive False Positive Cases from Cell-Free Fetal DNA Testing in a Single Referring Centre. J. Prenat. Med. 2014, 8, 31–35.
  62. Ilie, M.; Hofman, V.; Long, E.; Bordone, O.; Selva, E.; Washetine, K.; Marquette, C.H.; Hofman, P. Current Challenges for Detection of Circulating Tumor Cells and Cell-Free Circulating Nucleic Acids, and Their Characterization in Non-Small Cell Lung Carcinoma Patients. What Is the Best Blood Substrate for Personalized Medicine? Ann. Transl. Med. 2014, 2, 107.
  63. Reichel, J.B.; McCormick, J.; Fromm, J.R.; Elemento, O.; Cesarman, E.; Roshal, M. Flow-sorting and Exome Sequencing of the Reed-Sternberg Cells of Classical Hodgkin Lymphoma. J. Vis. Exp. JoVE 2017, 124, 54399.
  64. Skinnider, B.F.; Elia, A.J.; Gascoyne, R.D.; Patterson, B.; Trumper, L.; Kapp, U.; Mak, T.W. Signal Transducer and Activator of Transcription 6 Is Frequently Activated in Hodgkin and Reed-Sternberg Cells of Hodgkin Lymphoma. Blood 2002, 99, 618–626.
  65. Desch, A.-K.; Hartung, K.; Botzen, A.; Brobeil, A.; Rummel, M.; Kurch, L.; Georgi, T.; Jox, T.; Bielack, S.; Burdach, S.; et al. Genotyping Circulating Tumor DNA of Pediatric Hodgkin Lymphoma. Leukemia 2020, 34, 151–166.
  66. Guo, Q.; Wang, J.; Xiao, J.; Wang, L.; Hu, X.; Yu, W.; Song, G.; Lou, J.; Chen, J. Heterogeneous Mutation Pattern in Tumor Tissue and Circulating Tumor DNA Warrants Parallel NGS Panel Testing. Mol. Cancer 2018, 17, 131.
  67. Wang, B.; Wu, S.; Huang, F.; Shen, M.; Jiang, H.; Yu, Y.; Yu, Q.; Yang, Y.; Zhao, Y.; Zhou, Y.; et al. Analytical and Clinical Validation of a Novel Amplicon-Based NGS Assay for the Evaluation of Circulating Tumor DNA in Metastatic Colorectal Cancer Patients. Clin. Chem. Lab. Med. CCLM 2019, 57, 1501–1510.
  68. Perdigones, N.; Murtaza, M. Capturing tumor heterogeneity and clonal evolution in solid cancers using circulating tumor DNA analysis. Pharmacol. Ther. 2017, 174, 22–26.
  69. McDonough, S.J.; Bhagwate, A.; Sun, Z.; Wang, C.; Zschunke, M.; Gorman, J.A.; Kopp, K.J.; Cunningham, J.M. Use of FFPE-Derived DNA in next Generation Sequencing: DNA Extraction Methods. PLoS ONE 2019, 14, e0211400.
  70. Swerdlow, S.H.; Campo, E.; Pileri, S.A.; Harris, N.L.; Stein, H.; Siebert, R.; Advani, R.; Ghielmini, M.; Salles, G.A.; Zelenetz, A.D.; et al. The 2016 Revision of the World Health Organization Classification of Lymphoid Neoplasms. Blood 2016, 127, 2375–2390.
  71. Diehl, V.; Sextro, M.; Franklin, J.; Hansmann, M.-L.; Harris, N.; Jaffe, E.; Poppema, S.; Harris, M.; Franssila, K.; van Krieken, J.; et al. Clinical Presentation, Course, and Prognostic Factors in Lymphocyte-Predominant Hodgkin’s Disease and Lymphocyte-Rich Classical Hodgkin’s Disease: Report From the European Task Force on Lymphoma Project on Lymphocyte-Predominant Hodgkin’s Disease. J. Clin. Oncol. 1999, 17, 776–783.
  72. Carbone, A.; Gloghini, A.; Aldinucci, D.; Gattei, V.; Dalla-Favera, R.; Gaidano, G. Expression pattern of MUM1/IRF4 in the spectrum of pathology of Hodgkin’s disease. Br. J. Haematol. 2002, 117, 366–372.
  73. Jiwa, N.M.; Kanavaros, P.; van der Valk, P.; Walboomers, J.M.; Horstman, A.; Vos, W.; Mullink, H.; Meijer, C.J. Expression of C-Myc and Bcl-2 Oncogene Products in Reed-Sternberg Cells Independent of Presence of Epstein-Barr Virus. J. Clin. Pathol. 1993, 46, 211–217.
  74. Tiacci, E.; Döring, C.; Brune, V.; van Noesel, C.J.M.; Klapper, W.; Mechtersheimer, G.; Falini, B.; Küppers, R.; Hansmann, M.-L. Analyzing Primary Hodgkin and Reed-Sternberg Cells to Capture the Molecular and Cellular Pathogenesis of Classical Hodgkin Lymphoma. Blood 2012, 120, 4609–4620.
  75. Devilard, E.; Bertucci, F.; Trempat, P.; Bouabdallah, R.; Loriod, B.; Giaconia, A.; Brousset, P.; Granjeaud, S.; Nguyen, C.; Birnbaum, D.; et al. Gene Expression Profiling Defines Molecular Subtypes of Classical Hodgkin’s Disease. Oncogene 2002, 21, 3095–3102.
  76. Traverse-Glehen, A.; Pittaluga, S.; Gaulard, P.; Sorbara, L.; Alonso, M.A.; Raffeld, M.; Jaffe, E.S. Mediastinal Gray Zone Lymphoma: The Missing Link between Classic Hodgkin’s Lymphoma and Mediastinal Large B-Cell Lymphoma. Am. J. Surg. Pathol. 2005, 29, 1411–1421.
  77. Giulino-Roth, L. How I treat primary mediastinal B-cell lymphoma. Blood 2018, 132, 782–790.
  78. Jardin, F.; Pujals, A.; Pelletier, L.; Bohers, E.; Camus, V.; Mareschal, S.; Dubois, S.; Sola, B.; Ochmann, M.; Lemonnier, F.; et al. Recurrent Mutations of the Exportin 1 Gene (XPO1) and Their Impact on Selective Inhibitor of Nuclear Export Compounds Sensitivity in Primary Mediastinal B-Cell Lymphoma: XPO1 Mutations in Primary Mediastinal B-Cell Lymphoma. Am. J. Hematol. 2016, 91, 923–930.
  79. Dunleavy, K.; Wilson, W.H. Primary mediastinal B-cell lymphoma and mediastinal gray zone lymphoma: Do they require a unique therapeutic approach? Blood 2015, 125, 33–39.
  80. Dunleavy, K.; Grant, C.; Eberle, F.C.; Pittaluga, S.; Jaffe, E.S.; Wilson, W.H. Gray zone lymphoma: Better treated like hodgkin lymphoma or mediastinal large B-cell lymphoma? Curr. Hematol. Malig. Rep. 2012, 7, 241–247.
  81. Van Slambrouck, C.; Huh, J.; Suh, C.; Song, J.Y.; Menon, M.P.; Sohani, A.R.; Duffield, A.S.; Goldberg, R.C.; Dama, P.; Kiyotani, K.; et al. Diagnostic Utility of STAT6YE361 Expression in Classical Hodgkin Lymphoma and Related Entities. Mod. Pathol. 2020, 33, 834–845.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
Upload a video for this entry
Information
Subjects: Pharmacology & Pharmacy; Oncology
Contributor MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Vincent Camus
View Times: 762
Revisions: 2 times (View History)
Update Date: 10 Mar 2021
Table of Contents
    1000/1000
    Hot Most Recent
    ScholarVision Creations
    Feedback