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Fingerprinting in cancer diagnostics

Dedicated to Professors Heyrovsky and Brdicka

Cancer, “a plague of 21st century”, threatens humans however at the same time presents a subject of investigation for broad scientific public. Successful treatment of the disease depends on numerous factors such as prevention, early and sensitive diagnostics. That means the sooner a cancer is detected, the better the chances to treat it effectively [1,2]. Thus, it is not surprising that new methods, arrays and techniques are still looked for this purpose.

History
Already in 1937, Rudolf Brdicka, published his discoveries about using polarography to diagnose a tumour disease in Nature [3]. He found out a sensitive polarographic „protein effect“ conspicuously exhibited by serum. The explained this phenomenon by catalytic activity of the sulphydryl groups of proteins. The „protein effect“, represented by a characteristic wave on the current voltage curve, has been always found larger in normal serum than in cancer serum sample [3]. One year later, Brdicka’s colleague Jaroslav Heyrovsky, Nobel Prize laureate in Chemistry 1959, published a paper in the same journal. In this work, he summarized results obtained in the field of Polarographic Research on Cancer [4]. Heyrovsky believed that this field of study would be of general interest of many scientific groups around the world. But it did not take place. Since then, electrochemistry has been slowly disappearing from tumour disease diagnostics while being replaced by modern analytical and molecular biology techniques. Thus, this unique and interesting method has not been used with a few exceptions [5] for more than fifty years.

Metallothioneins
These low molecular intracellular proteins rich in cysteine are able to bind heavy metals in its structure [6-9]. Generally accepted idea, that metallothioneins (MTs) are involved only in storage, homeostasis and detoxification of metal ions, has to be nowadays modified due recent findings that they are involved in apoptosis inhibition, immunomodulation, regulating of transcription, cells proliferation and enzymes activation via administration of zinc atoms to the proteins and regulation of its concentration. MT genes are regulated in a tissue- and isoform-specific way by numerous factors, including a general responsiveness to zinc and other dietary factors, inflammation, environmental stress and cell proliferation, which is related to cancer. Moreover the level of MT can be related to the efficacy of treatment with certain drugs, e.g. cancer chemotherapeutic agents. Another field receiving currently a considerable attention is the value of MTs as biomarkers of zinc status, metal exposure and the prognosis of certain cancers [10]. Moreover, there are some evidences that elevated heavy metal and MTs content in tumour tissues is connected to the increased invasivity and metastating of a tumour [11]. Besides the understanding of the role of MTs, essential and non-essential metals in carcinogenesis and tumour growth, the investigation of metal distribution within a tumour can give an answer to many important questions concerning the growth of the tumour and its regulation. Understanding of this phenomenon can subsequently lead to discovering of new approaches of tumour growth inhibition. For this purpose, combination of various analytical approaches is needed.

Mathematical analysis
Besides electrochemistry, techniques like immunochemistry, mass spectrometry and electrophoresis generate from tens to hundreds signals somehow relating to the sample composition but there is no complete model for behaviour of proteins. It is almost impossible to process all such data manually even with help of instrument software. It is probable that each proteome has to be characterized combining the information obtained by several independent analytical approaches. But finding such relational dependencies is out of scope of manual evaluation of provided data as it leads to combinatorial explosion [12]. This task cannot be resolved without efficient computer supported decision making based on results of sophisticated preliminary data-mining analysis that will design novel means for representation and interpretation of the considered data. Data-mining (DM) is being studied and applied for nearly two decades. Numerous methods for data processing and modelling have been designed, tested and are now available as commercially or as an open source tools ready to support analysis of typical datasets. However, the analysis of scientific data still remains a big challenge. Especially because each new task with its own specific requirements and constraints calls for design of a targeted data pre-processing approach, for novel data representation and for modelling solutions which make it possible to take into account the domain knowledge as a guide during the search for a reasonable hypothesis. The more methods there exist the more difficult is the task of the DM expert, because the expert needs some tools to assess quickly the properties of the provided data, to reveal attribute interactions and the type of relations hidden in the data: such information can help to choose the proper tools to be applied, identify interesting subsets, motivate enhancement of the considered data and point to some surprising properties of the considered data. Data visualization coupling human pattern recognition and problem-solving capabilities seems to offer an attractive assistance – that is why visual data mining attracts lot of attention recently. The challenge is to design and create clear, meaningful and integrated visualizations that support interaction between data-miners and data-producers who poses the domain knowledge which is necessary for identification of interesting directions for further data-mining activities.

Electrochemical fingerprinting
In our studies, we used the advantages of mathematical tools and electrochemical analysis of metallothionein. Primarily, we aimed at in vitro interactions studies of interactions of MT with cisplatin. To evaluate the results, interaction constants were suggested. Here, we found that the maximum increased interaction occurred, when conservative aminoacids were substituted for more than one position outside the cysteine cluster. On the contrary, aminoacid substitution within the cysteine cluster led to a reduction in interaction constants. This result clearly indicates that aminoacids outside cysteine binding motif are of high importance for interactions of metallothionein with anticancer drugs [13,14]. Mathematical approach was also utilized for evaluation and classification of datasets obtained by electrochemical determination of metallothionein in tissues. Based on our results, we were able to construct a decision tree distinguishing among electrochemical analysis data resulting from measurements of all the considered tissues [15]. It is obvious that there have been made many attempts to provide fingerprinting of a tumour disease, however, these attempts suffered from very complex matrices and large sets of unprocessed data. We have tested the potential of the visual data-mining approach to data provided by Brdicka’s curves recently. Our intention has been to distinguish various biological samples from diverse resources. The obtained results are more than encouraging [16]. They indicate that fingerprints for some cancers could be based on results of sophisticated mathematical transformation of Brdicka’s signals. It is obvious that mathematical treatment of analytical signals using sophisticated mathematical tools could be very beneficial not only for chemists, biochemists and biologists, but also for mathematicians themselves. On the side of the “producers” of the data, there is possibility to discover some at first sight unknown phenomena. However, producing of tons of data without any idea only to “hunt” some ghosts in chromatograms, mass spectra, voltammograms and other types of analytical data representations is being more or less useless. There must be good hypothesis and then meaningfully used mathematical tools can possibly help to find so called The Holy Grail. Moreover, there is also possibility to use sophisticated mathematical tools also for data analysis as suggesting of a machine mimicking ribosome for nanotechnological synthesis of peptides [17]

Acknowledgement
The authors wish to express their thanks to Prof. Olga Stepankova, Dr Sona Krizkova and Dr. Lenka Vyslouzilova for helpful discussions and suggestions.

References
1. Polzer B, Klein CA. The challenges of targeting minimal residual cancer. Nat. Med., 19(3), 274-275 (2013).
2. Anonymous. Focusing on the cell biology of cancer. Nat. Cell Biol., 15(1), 1-1 (2013).
3. Brdicka R. Application of the polarographic effect of proteins in cancer diagnosis. Nature, 139, 330-330 (1937).
4. Heyrovsky J. Polarographic research in cancer. Nature, 142(3590), 317-319 (1938).
5. Olafson RW, Sim RG. Electrochemical approach to quantitation and characterization of metallothioneins. Anal. Biochem., 100(2), 343-351 (1979).
6. Dallinger R, Berger B, Hunziker P, Kagi JHR. Metallothionein in snail Cd and Cu metabolism. Nature, 388(6639), 237-238 (1997).
7. Meloni G, Sonois V, Delaine T et al. Metal swap between Zn(7)-metallothionein-3 and amyloid-beta-Cu protects against amyloid-beta toxicity. Nat. Chem. Biol., 4(6), 366-372 (2008).
8. Robinson NJ. A bacterial copper metallothionein. Nat. Chem. Biol., 4(10), 582-583 (2008).
9. Ruttkay-Nedecky B, Nejdl L, Gumulec J et al. The role of metallothionein in oxidative stress. Int. J. Mol. Sci., 14(3), 6044-6066 (2013).
10. Krizkova S, Ryvolova M, Hrabeta J et al. Metallothioneins and zinc in cancer diagnosis and therapy. Drug Metab. Rev., 44(4), 287-301 (2012).
11. Babula P, Masarik M, Adam V et al. Mammalian metallothioneins: properties and functions. Metallomics, 4(8), 739-750 (2012).
12. Klema J, Novakova L, Karel F, Stepankova O, Zelezny F. Sequential data mining: A comparative case study in development of atherosclerosis risk factors. IEEE Trans. Syst. Man Cybern. Part C-Appl. Rev., 38(1), 3-15 (2008).
13. Zitka O, Kominkova M, Skalickova S et al. Hydrodynamic Voltammograms Profiling of Metallothionein Fragment. Int. J. Electrochem. Sci., 7(11), 10544-10561 (2012).
14. Zitka O, Kominkova M, Skalickova S et al. Single amino acid change in metallothionein metal-binding cluster influences interaction with cisplatin. Int. J. Electrochem. Sci., 8(2), 2625-2634 (2013).
15. Sobrova P, Vyslouzilova L, Stepankova O et al. Tissue specific electrochemical fingerprinting. PLoS ONE, 7(11), 1-12, e49654 (2012).
16. Vyslouzilova L, Krizkova S, Anyz J et al. Using of brightness wavelet transformation for automated analysis of serum metallothioneins and zinc-containing-proteins by western blots to subclassify the childhood solid tumours. Electrophoresis, in press (2013).
17. Lewandowski B, De Bo G, Ward JW et al. Sequence-Specific Peptide Synthesis by an Artificial Small-Molecule Machine. Science, 339(6116), 189-193 (2013).

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