*All tables, charts, graphs and pictures that are featured in this article can be found in the .pdf attachment at the end of the paper. 

Introduction

Cancer is among the main causes of morbidity and mortality in the Slovak Republic. From all countries of the European Union, Slovakia is one of the most affected countries by this disease with a death rate 324.05 people per 100,000 residents(1) (Table 1). Cancer affects patients and their relatives as well, therefore it leads to serious health and social problems. In addition, it also has an impact on the financing of health care and the national economy of the country.

Due to the asymptomatic onset of cancer, most oncological patients are identified at advanced stage of disease and many times only by random diagnoses(2). This significantly worse the outcome of antitumor therapy, which already varies between patients. In addition, during tumor therapy, tumor cells are under strong selection pressure, therefore their genome is dynamically evolving, and within one tumor, different cell populations can be formed that can be resistant to the selected therapy(3). Due to the heterogeneity between various parts of the tumor, information obtained through tissue biopsy is often misrepresenting. Therefore, it would be necessary to obtain a tissue sample from multiple parts of the tumor and from all tumors that are present in the organism. This requires a very invasive procedure that leads to physical and mental burden on patients(4). These findings suggest that key factor for successful cancer treatment is early diagnosis of patient before the onset of the aggressive phenotype of disease. Consequently, it is important to determine the appropriate treatment for each patient to minimize the side effects and it is also necessary to continuously and effectively monitor the progression of the disease in order to respond as quickly as possible to the developing cells.

These are reasons why there is a rising need for biomarkers to diagnose cancer, to monitor cancer progression and to predict patient response to therapy(5). Appropriate solution are biomarkers usable for a liquid biopsy, which appears to be sufficiently representative, and much less invasive method compared to tissue biopsy. Liquid biopsy consists of the analysis of body fluids such as blood, urine, saliva, or cerebrospinal fluid that contain genetic information derived from tumor tissue. The most attention is currently attributed to the analysis of blood plasma because nucleic acids, proteins, cells and extracellular vesicles (EVs) gets to the bloodstream from all parts of the human body and in a case of oncological patients also from the tumor tissue(6) (Figure 1). In the future, this area of medicine could be called “Liquid Dynamic Medicine”. This concept represents this methodological approach because it describes the dynamic changes in tumor development that we must take into account in tumor therapy, whereas information about these changes is obtained from the body fluids of patient(3).

Cell-free DNA

Cell free DNA (cfDNA) represent 150-180 bp long fragments which are released from cells into the bloodstream through apoptosis and necrosis. It has been shown that higher levels of cfDNA are found in the blood of oncological patients than in healthy subjects(7). cfDNA that is released from tumor cells (ctDNA) contain the molecular characteristics of primary tumors and therefore their analysis could have a potential for the diagnosis and monitoring of cancer by liquid biopsy(8). Experimental studies have confirmed that tumor-specific mutations can be detected with high sensitivity by massive parallel sequencing (MPS) in ctDNA(7). In a breast cancer patient, mutation profile of ctDNA, primary tumor and metastasis was compared by targeted MPS. In metastatic lesion, there were identified mutations that could not be detected in the primary tumor. However, sequencing of ctDNA could capture all mutations that were present in both the primary tumor and metastases. This study provided evidence that ctDNA contains representative tumor genetic material and can solve the problem of intra-tumor heterogeneity(9). Whole genome sequencing approaches have also been published to identify tumor-specific chromosomal abnormalities in cfDNA of breast and colorectal cancer patients. It has been shown that using this approach, ctDNA with concentration of ≥ 0.75% can be detected in the blood of oncological patients with a 90% sensitivity and > 99% specificity(10). Both the targeted and whole genome analysis of ctDNA has been proven, but because of the high cost of whole genome analysis, the research focuses on targeted MPS analyzes that are more cost-effective for routine diagnostic practice.

Cell-free RNA

Cell-free RNAs (cfRNAs) are instable molecules that are subjected to degradation, resulting in relatively low levels in the blood. Because of these limitations, cfRNA is poorly explored and do not receive much attention. However, a recent study has shown that circulating mRNA and microRNA can be used as effective diagnostic markers for prostate cancer. Four circulating candidate molecules (miR-200c, miR-200 b, OR51E2 and SIM2) were used to predict cancer risk, which were able to diagnose prostate cancer with 67% sensitivity and 75% specificity(11).

MicroRNAs (miRNAs) are small non-coding RNA molecules that play a significant role in regulating important physiological processes in the human body. Cell-free miRNA can be enclosed in EVs, especially exosomes, but also occurs in complexes with ribonucleoproteins Argonaute 2 or Nucleomorphin 1. These ribonucleoprotein complexes protect miRNAs from degradation so they represent stable circulating biomarkers(12,13,14). Many studies have shown that impaired expression of some miRNAs is associated with the development of cancer, therefore quantification of these molecules could be an effective non-invasive tool for the detection and monitoring of tumor diseases(7,15,16). In addition, experiments on animal models have shown that if the impaired levels of certain miRNA molecules recover to normal values, it is possible to alleviate the symptoms of the disease or even eliminate the disease. Thus, miRNA molecules have potential in cancer therapy(17).

Extracellular vesicles

EVs are membrane structures that mediate intercellular communication(18). Based on their size and origin, they are divided into several categories including exosomes, microvesicles and apoptotic bodies(19). These vesicles can be released from both healthy and tumor cells, but tumor cells have been shown to release more EVs compared to normal cells. EVs that are released from tumor cells are called oncosomes because they contain various oncogenic components in the form of oncoproteins and nucleic acids(20,21). When oncosome is introduced into recipient cells, oncogenic factors trigger signaling pathways that can transfer the aggressive phenotype of tumor cells to idle cells(19). Since EVs serve as carriers of macromolecules, they contain proteins, lipids and nucleic acids that are protected from degradation(22). So, this is the reason why high-quality RNA molecules can be extracted by exosomes. Another benefit of these vesicles is, that they originate from living cells, so they represent the active processes running in the cell(7).

Conclusion

Statistical data of mortality showed that the current situation in the field of cancer is very poor in Slovakia and also within the European Union. In 2014, up to 24% of all deaths were caused by cancer. Outdated procedures used in clinical practice are also responsible for the worse situation in Slovakia. Therefore, it is necessary to introduce methods for detection and monitoring of tumor diseases that would help to early identification of oncological patients and avoiding the side effects of therapy. The introduction of liquid biopsy method and next generation sequencing technology for routine clinical practice should contribute to the overall improvement and personalization of anticancer therapy.

REFERENCES

1. http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=hlth_cd_ asdr2 & lang=en
2. Maroto P, Rini B. Molecular biomarkers in advanced renal cell carcino- Clin Cancer Res 2014; 20(8): 2060-2071.
3. Silvestris N, Ciliberto G, De Paoli P, et Liquid dynamic medicine and N-of-1 clinical trials: a change of perspective in oncology research. J Exp Clin Cancer Res 2017; 36(1): 128.
4. Ngo TC, Wood CG, Karam J Biomarkers of renal cell carcinoma. Urol Oncol 2014; 32(3): 243-251.
5. Zheng J, Wang L, Peng Z, et Low level of PDZ domain containing 1 (PDZK1) predicts poor clinical outcome in patients with clear cell renal cell carcinoma. EBioMedicine 2017; 15: 62-72.
6. Siravegna G, Marsoni S, Siena S, et Integrating liquid biopsies into the management of cancer. Nat Rev Clin Oncol. 2017; 14(9):531-548.
7. Brock G, Castellanos-Rizaldos E, Hu L, et Liquid biopsy for cancer screening, patient stratification and monitoring. Translational Cancer Re- search 2015; 4(3): 280-290.
8. Chang Y, Tolani B, Nie X, et Review of the clinical applications and technological advances of circulating tumor DNA in cancer monitoring. Ther Clin Risk Manag 2017; 13: 1363-1374.
9. De Mattos-Arruda L, Weigelt B, Cortes J, et Capturing intra-tumor genetic heterogeneity by de novo mutation profiling of circulating cellfree tumor DNA: a proof-of-principle. Ann Oncol 2014; 25(9): 1729-1735.
10. Leary RJ, Sausen M, Kinde I, et Detection of chromosomal alterations in the circulation of cancer patients with whole-genome sequencing. Sci Transl Med 2012; 4(162): 162ra154.
11. Souza MF, Kuasne H, Barros-Filho MC, et Circulating mRNAs and miRNAs as candidate markers for the diagnosis and prognosis of prostate cancer. PLoS One 2017; 12(9): e0184094.
12. Huang X, Yuan T, Tschannen M, et Characterization of human plasma-derived exosomal RNAs by deep sequencing. BMC Genomics 2013; 14: 319.
13. Creemers EE, Tijsen AJ, Pinto Circulating microRNAs: novel biomarkers and extracellular communicators in cardiovascular disease? Circ Res 2012; 110: 483-495.
14. Desgagné V, Guérin R, Guay SP, et Changes in high-density lipoprotein-carried miRNA contribution to the plasmatic pool after consumption of dietary trans fat in healthy men. Epigenomics 2017; 9: 669-688.
15. Morello M, Minciacchi VR, de Candia P, et Large oncosomes mediate intercellular transfer of functional microRNA. Cell Cycle 2013; 12(22): 3526-3536.
16. Londin E, Loher P, Telonis G, et al. Analysis of 13 cell types reveals evidence for the expression of numerous novel primate- and tissue-specific microRNAs. Proc Natl Acad Sci U S A 2015; 112(10): E1106-15.
17. Wahid F, Shehzad A, Khan T, et MicroRNAs: synthesis, mechanism, function, and recent clinical trials. Biochim Biophys Acta. 2010; 1803(11): 1231-1243.
18. Wan Y, Cheng G, Liu X, et Rapid magnetic isolation of extracellular vesicles via lipid-based nanoprobes. Nat Biomed Eng 2017;1. pii: 0058.
19. Wu K, Xing F, Wu SY, et Extracellular vesicles as emerging targets in cancer: Recent development from bench to bedside. Biochim Biophys Acta 2017; pii: S0304-419X(17)30149-X.
20. Di Vizio D, Kim J, Hager MH, et Oncosome formation in prostate cancer: association with a region of frequent chromosomal deletion in metastatic disease. Cancer Res. 2009; 69(13): 5601-5609.
21. Choi D, Lee TH, Spinelli C, et Extracellular vesicle communication pathways as regulatory targets of oncogenic transformation. Semin Cell Dev Biol 2017; 67: 11-22.
22. Contreras-Naranjo JC, Wu HJ, Ugaz VM. Microfluidics for exosome isolation and analysis: enabling liquid biopsy for personalized Lab Chip 2017; doi: 10.1039/c7lc00592j.