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PMTDS: a computational method based on genetic interaction networks for Precision Medicine Target-Drug Selection in cancer

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Abstract

Background

Precision medicine attempts to tailor the right therapy for the right patient. Recent progress in large-scale collection of patents’ tumor molecular profiles in The Cancer Genome Atlas (TCGA) provides a foundation for systematic discovery of potential drug targets specific to different types of cancer. However, we still lack powerful computational methods to effectively integrate multiple omics data and protein-protein interaction network technology for an optimum target and drug recommendation for an individual patient.

Methods

In this study, a computation method, Precision Medicine Target Drug Selection (PMTDS) based on genetic interaction networks is developed to select the optimum targets and associated drugs for precision medicine style treatment of cancer. The PMTDS system includes three parts: a personalized medicine knowledgebase for each cancer type, a genetic interaction network-based algorithm and a single patient molecular profiles. The knowledgebase integrates cancer drugs, drug-target databases and gene biological pathway networks. The molecular profiles of each tumor consists of DNA copy number alteration, gene mutation, and tumor gene expression variation compared to its adjacent normal tissue.

Results

The novel integrated PMTDS system is applied to select candidate target-drug pairs for 178 TCGA pancreatic adenocarcinoma (PDAC) tumors. The experiment results show known drug targets (EGFR, IGF1R, ERBB2, NR1I2 and AKR1B1) of PDAC treatment are identified, which provides important evidence of the PMTDS algorithm’s accuracy. Other potential targets PTK6, ATF, SYK are, also, recommended for PDAC. Further validation is provided by comparison of selected targets with, both, cell line molecular profiles from the Cancer Cell Line Encyclopedia (CCLE) and drug response data from the Cancer Therapeutics Response Portal (CTRP). Results from experimental analysis of forty six individual pancreatic cancer samples show that drugs selected by PMTDS have more sample-specific efficacy than the current clinical PDAC therapies.

Conclusions

A novelty target and drug priority algorithm PMTDS is developed to identify optimum target-drug pairs by integrating the knowledgebase base with a single patient’s genomics. The PMTDS system provides an accurate and reliable source for target and off-label drug selection for precision cancer medicine.

References

  1. 1.

    Le Tourneau, C., Kamal, M., Tsimberidou, A.-M., Bedard, P., Pierron, G., Callens, C., Rouleau, E., Vincent-Salomon, A., Servant, N., Alt, M., et al. (2015) Treatment algorithms based on tumor molecular profiling: the essence of precision medicine trials. J. Natl. Cancer Inst., 108, djv362 https://doi.org/10.1093/jnci/ djv362

  2. 2.

    Ciardiello, F., Arnold, D., Casali, P. G., Cervantes, A., Douillard, J.-Y., Eggermont, A., Eniu, A., McGregor, K., Peters, S., Piccart, M., et al., (2014) Delivering precision medicine in oncology today and in future-the promise and challenges of personalised cancer medicine: a position paper by the European Society for Medical Oncology (ESMO). Ann. Oncol., 25, 1673–1678

  3. 3.

    Schork, N. J. (2015) Personalized medicine: time for one-person trials. Nature, 520, 609–611

  4. 4.

    Le Tourneau, C., Delord, J.-P., Gonçalves, A., Gavoille, C., Dubot, C., Isambert, N., Campone, M., Trédan, O., Massiani, M.-A., Mauborgne, C., et al. (2015) Molecularly targeted therapy based on tumour molecular profiling versus conventional therapy for advanced cancer (SHIVA): a multicentre, open-label, proof-ofconcept, randomised, controlled phase 2 trial. Lancet Oncol., 16, 1324–1334

  5. 5.

    Rodon, J., Soria, J. C., Berger, R., Batist, G., Tsimberidou, A., Bresson, C., Lee, J. J., Rubin, E., Onn, A., Schilsky, R. L., et al. (2015) Challenges in initiating and conducting personalized cancer therapy trials: perspectives from WINTHER, a Worldwide Innovative Network (WIN) Consortium trial. Ann. Oncol., 26, 1791–1798

  6. 6.

    Tsimberidou, A.-M., Wen, S., Hong, D. S., Wheler, J. J., Falchook, G. S., Fu, S., Piha-Paul, S., Naing, A., Janku, F., Aldape, K. et al. (2014) Personalized medicine for patients with advanced cancer in the phase I program at MDAnderson: validation and landmark Analyses. Clin. Cancer Res., 20, 4827–4836

  7. 7.

    Mullard, A. (2015) NCI-MATCH trial pushes cancer umbrella trial paradigm. Nat. Rev. Drug Discov., 14, 513–515

  8. 8.

    Kandoth, C., McLellan, M. D., Vandin, F., Ye, K., Niu, B., Lu, C., Xie, M., Zhang, Q., McMichael, J. F., Wyczalkowski, M. A., et al. (2013) Mutational landscape and significance across 12 major cancer types. Nature, 502, 333–339

  9. 9.

    Zack, T. I., Schumacher, S. E., Carter, S. L., Cherniack, A. D., Saksena, G., Tabak, B., Lawrence, M. S., Zhang, C. Z., Wala, J., Mermel, C. H., et al. (2013) Pan-cancer patterns of somatic copy number alteration. Nat. Genet., 45, 1134–1140

  10. 10.

    Chin, L., Andersen, J. N. and Futreal, P. A. (2011) Cancer genomics: from discovery science to personalized medicine. Nat. Med., 17, 297–303

  11. 11.

    Conti, R. M., Bernstein, A. C., Villaflor, V. M., Schilsky, R. L., Rosenthal, M. B. and Bach, P. B. (2013) Prevalence of off-label use and spending in 2010 among patent-protected chemotherapies in a population-based cohort of medical oncologists. J. Clin. Oncol., 31, 1134–1139

  12. 12.

    Gupta, S. K. and Nayak, R. P. (2014) Off-label use of medicine: Perspective of physicians, patients, pharmaceutical companies and regulatory authorities. J. Pharmacol. Pharmacother., 5, 88–92

  13. 13.

    Witkiewicz, A. K., McMillan, E. A., Balaji, U., Baek, G., Lin, W.-C., Mansour, J., Mollaee, M., Wagner, K.-U., Koduru, P., Yopp, A., et al. (2015) Whole-exome sequencing of pancreatic cancer defines genetic diversity and therapeutic targets. Nat. Commun., 6, 6744

  14. 14.

    Barretina, J., Caponigro, G., Stransky, N., Venkatesan, K., Margolin, A. A., Kim, S., Wilson, C. J., Lehár, J., Kryukov, G. V., Sonkin, D., et al. (2012) The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature, 483, 603–607

  15. 15.

    Seashore-Ludlow, B., Rees, M. G., Cheah, J. H., Cokol, M., Price, E. V., Coletti, M. E., Jones, V., Bodycombe, N. E., Soule, C. K., Gould, J., et al. (2015) Harnessing connectivity in a large-scale small-molecule sensitivity dataset. Cancer Discov., 5, 1210–1223

  16. 16.

    Vargas, A. J. and Harris, C. C. (2016) Biomarker development in the precision medicine era: lung cancer as a case study. Nat. Rev. Cancer, 16, 525–537

  17. 17.

    Jameson, J. L. and Longo, D. L. (2015) Precision medicine — personalized, problematic, and promising. N. Engl. J. Med., 372, 2229–2234

  18. 18.

    Cheng, F., Hong, H., Yang, S. and Wei, Y. (2016) A bioinformatics approach for precision medicine off-label drug drug selection among triple negative breast cancer patients. J. Am. Med. Inform. Assoc., 23, 741–749

  19. 19.

    Cheng, F., Hong, H., Yang, S. and Wei, Y. (2017) Individualized network-based drug repositioning infrastructure for precision oncology in the panomics era. Brief. Bioinformatics, 18, 682–697

  20. 20.

    Tang, H., Zhong, F., Liu,W., He, F. and Xie, H. (2015) PathPPI: an integrated dataset of human pathways and protein-protein interactions. Sci. China Life Sci., 58, 579–589

  21. 21.

    Wang, K., Singh, D., Zeng, Z., Coleman, S. J., Huang, Y., Savich, G. L., He, X., Mieczkowski, P., Grimm, S. A., Perou, C. M., et al. (2010) MapSplice: accurate mapping of RNA-seq reads for splice junction discovery. Nucleic Acids Res., 38, e178

  22. 22.

    Li, B., Ruotti, V., Stewart, R. M., Thomson, J. A. and Dewey, C. N. (2010) RNA-Seq gene expression estimation with read mapping uncertainty. Bioinformatics, 26, 493–500

  23. 23.

    Robinson, M. D., McCarthy, D. J. and Smyth, G. K. (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26, 139–140

  24. 24.

    Hidalgo, M. (2012) New insights into pancreatic cancer biology. Ann. Oncol., 23, x135–x138

  25. 25.

    Kruger, S., Haas, M., Ormanns, S., Bächmann, S., Siveke, J. T., Kirchner, T., Heinemann, V. and Boeck, S. (2014) Translational research in pancreatic ductal adenocarcinoma: current evidence and future concepts. World J. Gastroenterol., 20, 10769–10777

  26. 26.

    Chung, Y. T., Matkowskyj, K. A., Li, H., Bai, H., Zhang, W., Tsao, M.-S., Liao, J. and Yang, G.-Y. (2012) Overexpression and oncogenic function of aldo-keto reductase family 1B10 (AKR1B10) in pancreatic carcinoma. Mod. Pathol., 25, 758–766

  27. 27.

    Philip, P. A. (2008) Targeted therapies for pancreatic cancer. Gastrointest. Cancer. Res., 2, S16–S19

  28. 28.

    Scott, A. J. and Wilkinson, J. C. (2016) HNF1A, KRT81, and CYP3A5: three more straws on the back of pancreatic cancer? Transl. Cancer Res., 5, S253–S256

  29. 29.

    Thompson, M. R., Xu, D. and Williams, B. R. G. (2009) ATF3 transcription factor and its emerging roles in immunity and cancer. J. Mol. Med. (Berl.), 87, 1053–1060

  30. 30.

    Simeone, D. M., Ji, B., Banerjee, M., Arumugam, T., Li, D., Anderson, M. A., Bamberger, A. M., Greenson, J., Brand, R. E., Ramachandran, V., et al. (2007) CEACAM1, a novel serum biomarker for pancreatic cancer. Pancreas, 34, 436–443

  31. 31.

    Harsha, H. C., Kandasamy, K., Ranganathan, P., Rani, S., Ramabadran, S., Gollapudi, S., Balakrishnan, L., Dwivedi, S. B., Telikicherla, D., Selvan, L. D. N., et al. (2009) A compendium of potential biomarkers of pancreatic cancer. PLoS Med., 6, e1000046

  32. 32.

    Huang, Y.-H., Zhu, C., Kondo, Y., Anderson, A. C., Gandhi, A., Russell, A., Dougan, S. K., Petersen, B.-S., Melum, E., Pertel, T., et al. (2015) CEACAM1 regulates TIM-3-mediated tolerance and exhaustion. Nature, 517, 386–390

  33. 33.

    Saloman, J. L., Albers, K. M., Li, D., Hartman, D. J., Crawford, H. C., Muha, E. A., Rhim, A. D. and Davis, B. M. (2016) Ablation of sensory neurons in a genetic model of pancreatic ductal adenocarcinoma slows initiation and progression of cancer. Proc. Natl. Acad. Sci. USA, 113, 3078–3083

  34. 34.

    Ono, H., Basson, M. D. and Ito, H. (2015) PTK6 potentiates gemcitabine-induced apoptosis by prolonging s-phase and enhancing DNA damage in pancreatic cancer. Mol. Cancer Res., 13, 1174–1184

  35. 35.

    Ono, H., Basson, M. D. and Ito, H. (2014) PTK6 promotes cancer migration and invasion in pancreatic cancer cells dependent on ERK signaling. PLoS One, 9, e96060

  36. 36.

    Middleton, G., Palmer, D. H., Greenhalf, W., Ghaneh, P., Jackson, R., Cox, T., Evans, A., Shaw, V. E., Wadsley, J., Valle, J. W., et al. (2017) Vandetanib plus gemcitabine versus placebo plus gemcitabine in locally advanced or metastatic pancreatic carcinoma (ViP): a prospective, randomised, double-blind, multicentre phase 2 trial. Lancet Oncol., 18, 486–499

  37. 37.

    Tomczak, K., Czerwinska, P. and Wiznerowicz, M. (2015) The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemp. Oncol. (Pozn.), 19, A68–A77

  38. 38.

    Wang, K., Li, M. and Hakonarson, H. (2010) ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res., 38, e164

  39. 39.

    Wishart, D. S., Knox, C., Guo, A. C., Shrivastava, S., Hassanali, M., Stothard, P., Chang, Z. and Woolsey, J. (2006) DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res., 34, D668–D672

  40. 40.

    Wishart, D. S., Knox, C., Guo, A. C., Cheng, D., Shrivastava, S., Tzur, D., Gautam, B. and Hassanali, M. (2008) DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res., 36, D901–D906

  41. 41.

    Bruford, E. A., Lush, M. J., Wright, M. W., Sneddon, T. P., Povey, S. and Birney, E. (2008) The HGNC Database in 2008: a resource for the human genome. Nucleic Acids Res., 36, D445–D448

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Acknowledgements

This work was supported by NIH Funding 1U54HD090215-01.

Author information

Correspondence to Lang Li or Lijun Cheng.

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Vasudevaraja, V., Renbarger, J., Shah, R.G. et al. PMTDS: a computational method based on genetic interaction networks for Precision Medicine Target-Drug Selection in cancer. Quant Biol 5, 380–394 (2017). https://doi.org/10.1007/s40484-017-0126-1

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Keywords

  • precision medicine
  • drug target
  • algorithm
  • pancreatic adenocarcinoma
  • biological pathway
  • cancer