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Oncobox Method for Scoring Efficiencies of Anticancer Drugs Based on Gene Expression Data

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Nucleic Acid Detection and Structural Investigations

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2063))

Abstract

We describe here the Oncobox method for scoring efficiencies of anticancer target drugs (ATDs) using high throughput gene expression data. The method rationale, design, and validation are given along with the examples of its practical applications in biomedicine. The method is based on the analysis of intracellular molecular pathways activation and measuring expressions of molecular target genes for every ATD under consideration. Using Oncobox method requires collection of normal (control) expression profiles and annotated databases of molecular pathways and drug target genes. Both microarray and RNA sequencing profiles are acceptable, although the latter type of data prevails in the most recent applications of this technique.

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Abbreviations

ATD:

Anticancer target drug

IMP:

Intracellular molecular pathway

NGS:

Next generation sequencing

PAL:

Pathway activation level, calculated using mRNA or protein expression data

References

  1. Hanna N, Einhorn LH (2014) Testicular cancer: a reflection on 50 years of discovery. J Clin Oncol 32:3085–3092

    Article  CAS  PubMed  Google Scholar 

  2. Oldenburg J, Aparicio J, Beyer J, Cohn-Cedermark G, Cullen M, Gilligan T et al (2015) Personalizing, not patronizing: the case for patient autonomy by unbiased presentation of management options in stage I testicular cancer. Ann Oncol 26:833–838

    Article  CAS  PubMed  Google Scholar 

  3. Ahles TA, Saykin AJ, Furstenberg CT, Cole B, Mott LA, Titus-Ernstoff L et al (2005) Quality of life of long-term survivors of breast cancer and lymphoma treated with standard-dose chemotherapy or local therapy. J Clin Oncol 23:4399–4405

    Article  CAS  PubMed  Google Scholar 

  4. Kayl AE, Meyers CA (2006) Side-effects of chemotherapy and quality of life in ovarian and breast cancer patients. Curr Opin Obstet Gynecol 18:24–28

    Article  PubMed  Google Scholar 

  5. Buzdin A, Sorokin M, Garazha A, Sekacheva M, Kim E, Zhukov N et al (2018) Molecular pathway activation – new type of biomarkers for tumor morphology and personalized selection of target drugs. Semin Cancer Biol 53:110–124

    Article  CAS  PubMed  Google Scholar 

  6. Druker BJ, Sawyers CL, Kantarjian H, Resta DJ, Reese SF, Ford JM et al (2001) Activity of a specific inhibitor of the BCR-ABL tyrosine kinase in the blast crisis of chronic myeloid leukemia and acute lymphoblastic leukemia with the Philadelphia chromosome. N Engl J Med 344:1038–1042

    Article  CAS  PubMed  Google Scholar 

  7. Druker BJ, Talpaz M, Resta DJ, Peng B, Buchdunger E, Ford JM et al (2001) Efficacy and safety of a specific inhibitor of the BCR-ABL tyrosine kinase in chronic myeloid leukemia. N Engl J Med 344:1031–1037

    Article  CAS  PubMed  Google Scholar 

  8. Spirin P, Lebedev T, Orlova N, Morozov A, Poymenova N, Dmitriev SE et al (2017) Synergistic suppression of t(8;21)-positive leukemia cell growth by combining oridonin and MAPK1/ERK2 inhibitors. Oncotarget 8:56991–57002

    Article  PubMed  PubMed Central  Google Scholar 

  9. Sjöström J (2002) Predictive factors for response to chemotherapy in advanced breast cancer. Acta Oncol 41:334–345

    Article  PubMed  Google Scholar 

  10. Aggarwal S (2010) Targeted cancer therapies. Nat Rev Drug Discov 9:427–428

    Article  CAS  PubMed  Google Scholar 

  11. Hudis CA (2007) Trastuzumab – mechanism of action and use in clinical practice. N Engl J Med 357:39–51

    Article  CAS  PubMed  Google Scholar 

  12. Nahta R, Esteva FJ (2007) Trastuzumab: triumphs and tribulations. Oncogene 26:3637–3643

    Article  CAS  PubMed  Google Scholar 

  13. Onitilo AA, Engel JM, Greenlee RT, Mukesh BN (2009) Breast cancer subtypes based on ER/PR and Her2 expression: comparison of clinicopathologic features and survival. Clin Med Res 7:4–13

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Institute for Quality and Efficiency in Health Care (2014) Curation vs. palliation: an attempt to clarify terms. Institute for Quality and Efficiency in Health Care (IQWiG), Cologne

    Google Scholar 

  15. Gridelli C, De Marinis F, Di Maio M, Cortinovis D, Cappuzzo F, Mok T (2011) Gefitinib as first-line treatment for patients with advanced non-small-cell lung cancer with activating epidermal growth factor receptor mutation: Review of the evidence. Lung Cancer 71:249–257

    Article  CAS  PubMed  Google Scholar 

  16. Hornberger J, Cosler LE, Lyman GH (2005) Economic analysis of targeting chemotherapy using a 21-gene RT-PCR assay in lymph-node-negative, estrogen-receptor-positive, early-stage breast cancer. Am J Manag Care 11:313–324

    PubMed  Google Scholar 

  17. Le Tourneau C, Paoletti X, Servant N, Bièche I, Gentien D, Rio Frio T et al (2014) Randomised proof-of-concept phase II trial comparing targeted therapy based on tumour molecular profiling vs conventional therapy in patients with refractory cancer: results of the feasibility part of the SHIVA trial. Br J Cancer 111:17–24

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Martel CL, Lara PN (2003) Renal cell carcinoma: current status and future directions. Crit Rev Oncol Hematol 45:177–190

    Article  PubMed  Google Scholar 

  19. Poole RM (2014) Pembrolizumab: first global approval. Drugs 74:1973–1981

    Article  CAS  PubMed  Google Scholar 

  20. Russell K, Shunyakov L, Dicke KA, Maney T, Voss A (2014) A practical approach to aid physician interpretation of clinically actionable predictive biomarker results in a multi-platform tumor profiling service. Front Pharmacol 5:76

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Green DE, Jayakrishnan TT, Hwang M, Pappas SG, Gamblin TC, Turaga KK (2014) Immunohistochemistry - microarray analysis of patients with peritoneal metastases of appendiceal or colorectal origin. Front Surg 1:50

    PubMed  Google Scholar 

  22. Popovtzer A, Sarfaty M, Limon D, Marshack G, Perlow E, Dvir A et al (2015) Metastatic salivary gland tumors: a single-center study demonstrating the feasibility and potential clinical benefit of molecular-profiling-guided therapy. Biomed Res Int 2015:614845

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Vigneswaran J, Tan Y-HC, Murgu SD, Won BM, Patton KA, Villaflor VM et al (2016) Comprehensive genetic testing identifies targetable genomic alterations in most patients with non-small cell lung cancer, specifically adenocarcinoma, single institute investigation. Oncotarget 7:18876–18886

    Article  PubMed  PubMed Central  Google Scholar 

  24. Blagosklonny MV (2013) MTOR-driven quasi-programmed aging as a disposable soma theory: blind watchmaker vs. intelligent designer. Cell Cycle 12:1842–1847

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Borisov N, Sorokin M, Garazha AV, Buzdin A (2019) Quantitation of molecular pathway activation using RNA sequencing data. In: Walker J (ed) Methods Molecular Biology. Springer, Heidelberg

    Google Scholar 

  26. Zolotovskaia M, Sorokin M, Garazha A, Borisov N, Buzdin A (2019) Molecular pathway analysis of mutation data for biomarkers discovery and scoring of target cancer drugs. In: Walker J (ed) Methods Molecular Biology. Springer, Heidelberg

    Google Scholar 

  27. Borisov N, Suntsova M, Sorokin M, Garazha A, Kovalchuk O, Aliper A et al (2017) Data aggregation at the level of molecular pathways improves stability of experimental transcriptomic and proteomic data. Cell Cycle 16:1810–1823

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Buzdin AA, Zhavoronkov AA, Korzinkin MB, Roumiantsev SA, Aliper AM, Venkova LS et al (2014) The OncoFinder algorithm for minimizing the errors introduced by the high-throughput methods of transcriptome analysis. Front Mol Biosci 1:8

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Artemov A, Aliper A, Korzinkin M, Lezhnina K, Jellen L, Zhukov N et al (2015) A method for predicting target drug efficiency in cancer based on the analysis of signaling pathway activation. Oncotarget 6:29347–29356

    Article  PubMed  PubMed Central  Google Scholar 

  30. Zhu Q, Izumchenko E, Aliper AM, Makarev E, Paz K, Buzdin AA et al (2015) Pathway activation strength is a novel independent prognostic biomarker for cetuximab sensitivity in colorectal cancer patients. Hum Genome Var 2:15009

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Venkova L, Aliper A, Suntsova M, Kholodenko R, Shepelin D, Borisov N et al (2015) Combinatorial high-throughput experimental and bioinformatic approach identifies molecular pathways linked with the sensitivity to anticancer target drugs. Oncotarget 6:27227–27238

    Article  PubMed  PubMed Central  Google Scholar 

  32. Buzdin AA, Prassolov V, Zhavoronkov AA, Borisov NM (2017) Bioinformatics meets biomedicine: OncoFinder, a quantitative approach for interrogating molecular pathways using gene expression data. Methods Mol Biol 1613:53–83

    Article  CAS  PubMed  Google Scholar 

  33. Buzdin A, Sorokin M, Glusker A, Garazha A, Poddubskaya E, Shirokorad V et al (2017) Activation of intracellular signaling pathways as a new type of biomarkers for selection of target anticancer drugs. J Clin Oncol 35:e23142–e23142

    Article  Google Scholar 

  34. Zolotovskaia MA, Sorokin MI, Roumiantsev SA, Borisov NM, Buzdin AA (2018) Pathway instability is an effective new mutation-based type of cancer biomarkers. Front Oncol 8:658

    Article  PubMed  Google Scholar 

  35. Croft D, Mundo AF, Haw R, Milacic M, Weiser J, Wu G et al (2014) The Reactome pathway knowledgebase. Nucleic Acids Res 42:D472–D477

    Article  CAS  PubMed  Google Scholar 

  36. Schaefer CF, Anthony K, Krupa S, Buchoff J, Day M, Hannay T et al (2009) PID: the pathway interaction database. Nucleic Acids Res 37:D674–D679

    Article  CAS  PubMed  Google Scholar 

  37. Kanehisa M, Goto S (2000) KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28:27–30

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Romero P, Wagg J, Green ML, Kaiser D, Krummenacker M, Karp PD (2005) Computational prediction of human metabolic pathways from the complete human genome. Genome Biol 6:R2

    Article  PubMed  Google Scholar 

  39. Nishimura D (2001) BioCarta. Biotech Software Internet Rep 2:117–120

    Article  Google Scholar 

  40. Borisov N, Shabalina I, Tkachev V, Sorokin M, Garazha A, Pulin A et al (2019) Shambhala: a platform-agnostic data harmonizer for gene expression data. BMC Bioinformatics 20:66

    Article  PubMed  PubMed Central  Google Scholar 

  41. Rudy J, Valafar F (2011) Empirical comparison of cross-platform normalization methods for gene expression data. BMC Bioinformatics 12:467

    Article  PubMed  PubMed Central  Google Scholar 

  42. Zolotovskaia MA, Sorokin MI, Emelianova AA, Borisov NM, Kuzmin DV, Borger P et al (2019) Pathway based analysis of mutation data is efficient for scoring target cancer drugs. Front Pharmacol 10:1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Law V, Knox C, Djoumbou Y, Jewison T, Guo AC, Liu Y et al (2014) DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res 42:D1091–D1097

    Article  CAS  PubMed  Google Scholar 

  44. Lamb J (2006) The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313:1929–1935

    Article  CAS  PubMed  Google Scholar 

  45. Wilkinson L (2011) ggplot2: elegant graphics for data analysis by WICKHAM, H. Biometrics 67:678–679

    Article  Google Scholar 

  46. Poddubskaya EV, Baranova MP, Allina DO, Smirnov PY, Albert EA, Kirilchev AP et al (2018) Personalized prescription of tyrosine kinase inhibitors in unresectable metastatic cholangiocarcinoma. Exp Hematol Oncol 7:21

    Article  PubMed  PubMed Central  Google Scholar 

  47. Borisov N, Buzdin AA, Zavoronkovs A, Aliper AM, Allina D, Kovalchuk O et al (2017) System, method, and software for improved drug efficacy and safety in a patient. US Patent US20170193176A1

    Google Scholar 

  48. Borisov N, Tkachev V, Suntsova M, Kovalchuk O, Zhavoronkov A, Muchnik I et al (2018) A method of gene expression data transfer from cell lines to cancer patients for machine-learning prediction of drug efficiency. Cell Cycle 17:486–491

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Borisov N, Tkachev V, Buzdin A, Muchnik I (2018) Prediction of drug efficiency by transferring gene expression data from cell lines to cancer patients. In: Rozonoer L, Mirkin B, Muchnik I (eds) Braverman readings in machine learning. Key ideas from inception to current state. Springer, Cham, pp 201–212

    Chapter  Google Scholar 

  50. Borisov N, Tkachev V, Muchnik I, Buzdin A (2017) Individual drug treatment prediction in oncology based on machine learning using cell culture gene expression data. ACM Press, New York, NY, pp 1–6

    Google Scholar 

  51. Poddubskaya E, Baranova M, Allina D, Sekacheva M, Makovskaia L, Kamashev D et al (2019) Personalized prescription of imatinib in recurrent granulosa cell tumor of the ovary: case report. Cold Spring Harb Mol Case Stud. https://doi.org/10.1101/mcs.a003434

    Article  PubMed  PubMed Central  Google Scholar 

  52. Snyder V, Reed-Newman TC, Arnold L, Thomas SM, Anant S (2018) Cancer stem cell metabolism and potential therapeutic targets. Front Oncol 8:203

    Article  PubMed  PubMed Central  Google Scholar 

  53. Zhang L, Zhang H, Ai H, Hu H, Li S, Zhao J et al (2018) Applications of machine learning methods in drug toxicity prediction. Curr Top Med Chem 18:987–997

    Article  CAS  PubMed  Google Scholar 

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Acknowledgments

This study was supported by the Russian Science Foundation grant 18-15-00061.

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Correspondence to Anton Buzdin .

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Tkachev, V., Sorokin, M., Garazha, A., Borisov, N., Buzdin, A. (2020). Oncobox Method for Scoring Efficiencies of Anticancer Drugs Based on Gene Expression Data. In: Astakhova, K., Bukhari, S. (eds) Nucleic Acid Detection and Structural Investigations. Methods in Molecular Biology, vol 2063. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0138-9_17

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  • DOI: https://doi.org/10.1007/978-1-0716-0138-9_17

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-0137-2

  • Online ISBN: 978-1-0716-0138-9

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