Postgenomic Medicine: Alternative to Biomarkers

Cover Page

Abstract


In the article relevance of high-performance postgenomic technologies is tackled. The intrinsic problems of the implementation of genomics, proteomics and metabolomics in routine clinical practice are considered. Further development of postgenomic medicine requires severe change in the current research approaches. Avenue for development of such approaches are illustrated by metabolome research of human blood plasma. The postgenomic biomarkers are pictured as molecular iceberg, greater part of which is inaccessible for detection with measurement methods. Due to diversity of protein forms the spectrum of molecular markers will always evaluate in terms of incompleteness and inconsistence regardless of technological development level. These properties of «big data» are typical of data intensive domains. Special computational methods are essential for data intensive analytics and hardly suitable for the evidence-based medicine.

A. V. Lisitsa

Orekhovich Institute of Biomedical Chemistry,
Moscow

Author for correspondence.
Email: lisitsa063@gmail.com

Russian Federation

Доктор биологических наук, член-корреспондент РАН, директор 

Адрес: 119121, Москва, ул. Погодинская, д. 10

E. A. Ponomarenko

Orekhovich Institute of Biomedical Chemistry,
Moscow

Email: 2463731@gmail.com

Russian Federation

Кандидат биологических наук, заведующая лабораторией анализа постгеномных данных

Адрес: 119121, Москва, ул. Погодинская, д. 10

P. G. Lokhov

Orekhovich Institute of Biomedical Chemistry,
Moscow

Email: lokhovpg@gmail.com

Russian Federation

Доктор биологических наук, заведующий лабораторией масс-спектрометрической метаболомной диагностики

Адрес: 119121, Москва, ул. Погодинская, д. 10, стр. 8

A. I. Archakov

Orekhovich Institute of Biomedical Chemistry,
Moscow

Email: archak@ibmc.msk.ru

Russian Federation

Доктор биологических наук, академик РАН, профессор, научный руководитель 

Адрес: 119121, Москва, ул. Погодинская, д. 10, стр. 8

  1. Орехович В.Н. Современные представления о белках и их значение в биологии и медицине. — М.: Знание; 1952. — 22 c. [Orekhovich VN. Sovremennye predstavleniya o belkakh i ikh znachenie v biologii i meditsine. Moscow: Znanie; 1952. 22 p. (In Russ).]
  2. Anderson NL. The clinical plasma proteome: a survey of clinical assays for proteins in plasma and serum. Clin Chem. 2010;56(2):177–185. doi: 10.1373/clinchem.2009.126706.
  3. Gutman S, Kessler LG. The US Food and Drug Administration perspective on cancer biomarker development. Nat Rev Cancer. 2006;6(7):565–571. doi: 10.1038/nrc1911.
  4. Veenstra TD. Where are all the biomarkers? Expert Rev Proteomics. 2011;8(6):681–683. doi: 10.1586/epr.11.60.
  5. Archakov A, Zgoda V, Kopylov A, et al. Chromosome-centric approach to overcoming bottlenecks in the Human Proteome Project. Expert Rev Proteomics. 2012;9(6):667–676. doi: 10.1586/epr.12.54.
  6. Archakov AI, Ivanov YD, Lisitsa AV, Zgoda VG. AFM fishing nanotechnology is the way to reverse the Avogadro number in proteomics. Proteomics. 2007;7(1):4–9. doi: 10.1002/pmic.200600467.
  7. Lisitsa A, Moshkovskii S, Chernobrovkin A, et al. Profiling proteoforms: promising follow-up of proteomics for biomarker discovery. Expert Rev Proteomics. 2014;11(1):121–129. doi: 10.1586/14789450.2014.878652.
  8. Trifonova O, Lokhov P, Archakov A. Postgenomics diagnostics: metabolomics approaches to human blood profiling. OMICS. 2013;17(11):550–559. doi: 10.1089/omi.2012.0121.
  9. Дедов И.И., Тюльпаков А.Н., Чехонин В.П., и др. Персонализированная медицина: современное состояние и перспективы // Вестник Российской академии медицинских наук. — 2012. — Т. 67. — №12. — С. 4–12. [Dedov II, Tyul’pakov AN, Chekhonin VP, et al. Personilized medicine: State-of-the-art and prospects. Annals of the Russian academy of medical sciences. 2012;67(12):4–12. (In Russ).] doi: 10.15690/vramn.v67i12.474.
  10. Lin PH, Kuo WH, Huang AC, et al. Multiple gene sequencing for risk assessment in patients with early-onset or familial breast cancer. Oncotarget. 2016;7(7):8310–8320. doi: 10.18632/oncotarget.7027.
  11. Lisitsa AV, Poverennaya EV, Ponomarenko EA, Archakov AI. The width of human plasma proteome compared with cancer cell line and bacteria. J Biomol Res Ther. 2015;4(4):1000132. doi: 10.4172/2167-7956.1000132.
  12. Archakov A, Ivanov Y, Lisitsa A, Zgoda V. Biospecific irreversible fishing coupled with atomic force microscopy for detection of extremely low-abundant proteins. Proteomics. 2009;9(5):1326–1343. doi: 10.1002/pmic.200800598.
  13. Liotta LA, Petricoin EF, 3rd. -Omics and cancer biomarkers: link to the biological truth or bear the consequences. Cancer Epidemiol Biomarkers Prev. 2012;21(8):1229–1235. doi: 10.1158/1055-9965.EPI-12-0635.
  14. Ponomarenko E, Baranova A, Lisitsa A, et al. The chromosome-centric human proteome project at FEBS Congress. Proteomics. 2014;14(2-3):147–152. doi: 10.1002/pmic.201300373.
  15. Venter JC, Smith HO, Adams MD. The sequence of the human genome. Clin Chem. 2015;61(9):1207–1208. doi: 10.1373/clinchem.2014.237016.
  16. Miroshnichenko YV, Prostova MA, Kvetinskaya AV, Lisitsa AV. Life sciences in Russia: Priorities in 2014–2020. Acta Naturae. 2015;7(3(26)):6–14.
  17. Bartlett JM, Stirling D. A short history of the polymerase chain reaction. In: Bartlett JM, Stirling D, editors. PCR Protocols. 2nd ed. Humana Press; 2003. p. 3–6. doi: 10.1385/1-59259-384-4:3.
  18. Мальсагова К.А., Иванов Ю.Д., Плешакова Т.О., и др. КНИ-нанопроволочный биосенсор для детекции белка d-nfat 1 // Биомедицинская химия. — 2015. — Т. 61. — № 4. — С. 462–467. [Malsagova KA, Ivanov YuD, Pleshakova TO, et al. SOI-nanowire biosensor for the detection of D-NFAT 1 protein. Biomed Khim. 2015;61(4):462−467. (In Russ).] doi: 10.18097/PBMC20156104462.
  19. Ivanov YD, Pleshakova T, Malsagova K, et al. Highly sensitive protein detection by combination of atomic force microscopy fishing with charge generation and mass spectrometry analysis. FEBS J. 2014;281(20):4705–4717. doi: 10.1111/febs.13011.
  20. Shugay M, Britanova OV, Merzlyak EM, et al. Towards error-free profiling of immune repertoires. Nat Methods. 2014;11(6):653–655. doi: 10.1038/nmeth.2960.
  21. Hori SS, Gambhir SS. Mathematical model identifies blood biomarker-based early cancer detection strategies and limitations. Sci Transl Med. 2011;3(109):109–116. doi: 10.1126/scitranslmed.3003110.
  22. Lokhov PG, Balashova EE, Voskresenskaya AA, et al. Mass spectrometric signatures of the blood plasma metabolome for disease diagnostics. Biomed Rep. 2016;4(1):122–126. doi: 10.3892/br.2015.548.
  23. Лохов П.Г., Маслов Д.Л., Трифонова О.П., и др. Масс-спектрометрический анализ низкомолекулярной фракции крови как способ унификации терапевтического лекарственного мониторинга // Биомедицинская химия. — 2015. — Т. 60. — № 2. — С. 201–216. [Lokhov PG, Maslov DL, Trifonova OP, et al. Mass spectrometry of blood low-molecular fraction as a method for unification of therapeutic drug monitoring. Biomed Khim. 2014;60(2):201–216. (In Russ).]
  24. Lokhov PG, Dashtiev MI, Moshkovskii SA, Archakov AI. Metabolite profiling of blood plasma of patients with prostate cancer. Metabolomics. 2010;6(1):156–163. doi: 10.1007/s11306-009-0187-x.
  25. Lokhov PG, Trifonova OP, Maslov DL, Archakov AI. Blood plasma metabolites and the risk of developing lung cancer in Russia. Eur J Cancer Prev. 2013;22(4):335–341. doi: 10.1097/CEJ.0b013e32835b3898.
  26. Lokhov PG, Trifonova OP, Maslov DL, et al. Diagnosing impaired glucose tolerance using direct infusion mass spectrometry of blood plasma. PLoS One. 2014;9(9):e105343. doi: 10.1371/journal.pone.0105343.
  27. Lokhov PG, Kharybin ON, Archakov AI. Diagnosis of lung cancer based on direct-infusion electrospray mass spectrometry of blood plasma metabolites. Int J Mass Spectrom. 2012;309:200–205. doi: 10.1016/j.ijms.2011.10.002.
  28. He MX. The synergy between bioinformatics and cognitive informatics. In: Buzatu C, editor. Modern computer applications in science and education: Proceedings of the 14th International Conference on Applied Computer Science (ACS ‘14); Cambridge, MA, USA. WSEAS Press; 2014. p. 258−262.
  29. Lisitsa AV, Stewart E, Kolker E. Is it time for cognitive bioinformatics? J Data Mining Genomics Proteomics. 2015;6:173. doi: 10.4172/2153-0602.1000173.
  30. Васильков А. Когнитивные технологии IBM: на пути к искусственному мозгу. Компьютерра [интернет]. 8.08.2013 [доступ от 14.03.2016]. Доступ по ссылке http://computerra.ru/78397/ibm-synapse-cognitive-computing. [Vasilkov A. Kognitivniye tekhnologii IBM: na puti k iskusstvennomu mozgu. Kompiyuterra [Internet]. 08.08.2013. Access date 14.03.2016. Available on: http://computerra.ru/78397/ibm-synapse-cognitive-computing (In Russ.)]

Supplementary files

There are no supplementary files to display.

Views

Abstract - 244

PDF (Russian) - 29

Cited-By


PlumX

Dimensions

Comments on this article

View all comments


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

This website uses cookies

You consent to our cookies if you continue to use our website.

About Cookies