Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Original Article
  • Published:

A tumor deconstruction platform identifies definitive end points in the evaluation of drug responses

Oncogene volume 35, pages 727–737 (2016)Cite this article

Subjects

Abstract

Tumor heterogeneity and the presence of drug-sensitive and refractory populations within the same tumor are almost never assessed in the drug discovery pipeline. Such incomplete assessment of drugs arising from spatial and temporal tumor cell heterogeneity reflects on their failure in the clinic and considerable wasted costs in the drug discovery pipeline. Here we report the derivation of a flow cytometry-based tumor deconstruction platform for resolution of at least 18 discrete tumor cell fractions. This is achieved through concurrent identification, quantification and analysis of components of cancer stem cell hierarchies, genetically instable clones and differentially cycling populations within a tumor. We also demonstrate such resolution of the tumor cytotype to be a potential value addition in drug screening through definitive cell target identification. Additionally, this real-time definition of intra-tumor heterogeneity provides a convenient, incisive and analytical tool for predicting drug efficacies through profiling perturbations within discrete tumor cell subsets in response to different drugs and candidates. Consequently, possible applications in informed therapeutic monitoring and drug repositioning in personalized cancer therapy would complement rational design of new candidates besides achieving a re-evaluation of existing drugs to derive non-obvious combinations that hold better chances of achieving remission.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6

Similar content being viewed by others

References

  1. Wang B, Rosano JM, Cheheltani R, Achary MP, Kiani MF . Towards a targeted multi-drug delivery approach to improve therapeutic efficacy in breast cancer. Expert Opin Drug Deliv 2010; 7: 1159–1173.

    Article  CAS  Google Scholar 

  2. Wani AA, Sharma N, Shouche YS, Bapat SA . Nuclear-mitochondrial genomic profiling reveals a pattern of evolution in epithelial ovarian tumor stem cells. Oncogene 2006; 25: 6336–6344.

    Article  CAS  Google Scholar 

  3. Navin NE, Hicks J . Tracing the tumor lineage. Mol Oncol 2010; 4: 267–283.

    Article  Google Scholar 

  4. Marusyk A, Polyak K . Tumor heterogeneity: causes and consequences. Biochim Biophys Acta 2010; 1805: 105–117.

    CAS  Google Scholar 

  5. Reya T, Morrison SJ, Clarke MF, Weissman IL . Stem cells, cancer, and cancer stem cells. Nature 2001; 414: 105–111.

    Article  CAS  Google Scholar 

  6. Li B, Senbabaoglu Y, Peng W, Yang ML, Xu J, Li JZ . Genomic estimates of aneuploid content in glioblastoma multiforme and improved classification. Clin Cancer Res 2012; 18: 5595–5605.

    Article  CAS  Google Scholar 

  7. Magee JA, Piskounova E, Morrison SJ . Cancer stem cells: impact, heterogeneity, and uncertainty. Cancer Cell 2012; 3: 283–296.

    Article  Google Scholar 

  8. Schoenborn JR, Nelson P, Fang M . Genomic profiling defines subtypes of prostate cancer with the potential for therapeutic stratification. Clin Cancer Res 2013; 15: 4058–4066.

    Article  Google Scholar 

  9. Fisher R, Pusztai L, Swanton C . Cancer heterogeneity: implications for targeted therapeutics. Br J Cancer 2013; 108: 479–485.

    Article  CAS  Google Scholar 

  10. Visvader JE, Lindeman GJ . Cancer stem cells: current status and evolving complexities. Cell Stem Cell 2012; 10: 717–728.

    Article  CAS  Google Scholar 

  11. Cree IA, Glaysher S, Harvey AL . Efficacy of anti-cancer agents in cell lines versus human primary tumour tissue. Curr Opin Pharmacol 2010; 4: 375–379.

    Article  Google Scholar 

  12. Bapat SA, Mali AM, Koppikar CB, Kurrey NK . Stem and progenitor-like cells contribute to the aggressive behavior of human epithelial ovarian cancer. Cancer Res 2005; 65: 3025–3029.

    Article  CAS  Google Scholar 

  13. Kusumbe AP, Bapat SA . Cancer stem cells and aneuploid populations within developing tumors are the major determinants of tumor dormancy. Cancer Res 2009; 69: 9245–1953.

    Article  CAS  Google Scholar 

  14. Kusumbe AP, Mali AM, Bapat SA . CD133-expressing stem cells associated with ovarian metastases establish an endothelial hierarchy and contribute to tumor vasculature. Stem Cells 2009; 27: 498–508.

    Article  CAS  Google Scholar 

  15. Bouchet BP, Bertholon J, Falette N, Audoynaud C, Lamblot C, Puisieux A et al. Paclitaxel resistance in untransformed human mammary epithelial cells is associated with an aneuploidy-prone phenotype. Br J Cancer 2007; 97: 1218–1224.

    Article  CAS  Google Scholar 

  16. Bian M, Fu J, Yan Y, Chen Q, Yang C, Shi Q et al. Short exposure to paclitaxel induces multipolar spindle formation and aneuploidy through promotion of acentrosomal pole assembly. Sci China Life Sci 2010; 53: 1322–1329.

    Article  CAS  Google Scholar 

  17. Kunos CA, Ferris G, Pyatka N, Pink J, Radivoyevitch T . Deoxynucleoside salvage facilitates DNA repair during ribonucleotide reductase blockade in human cervical cancers. Radiat Res 2011; 176: 425–433.

    Article  CAS  Google Scholar 

  18. Chiang Y, Davis RG, Vishwanatha JK . Altered expression of annexin II in human B-cell lymphoma cell lines. Biochim Biophys Acta 1996; 1313: 295–301.

    Article  Google Scholar 

  19. Ye NS, Zhang RL, Zhao YF, Feng X, Wang YM, Luo GA . Effect of 5-azacytidine on the protein expression of porcine bone marrow mesenchymal stem cells in vitro. Genomics Proteomics Bioinformatics 2006; 4: 18–25.

    Article  CAS  Google Scholar 

  20. Niu D, Sui J, Zhang J, Feng H, Chen WN . iTRAQ-coupled 2-D LC-MS/MS analysis of protein profile associated with HBV-modulated DNA methylation. Proteomics 2009; 9: 3856–3868.

    Article  CAS  Google Scholar 

  21. Diaz-Cano SJ . Tumor heterogeneity: mechanisms and bases for a reliable application of molecular marker design. Int J Mol Sci 2012; 13: 1951–2011.

    Article  CAS  Google Scholar 

  22. Cavanagh BL, Walker T, Norazit A, Meedeniya AC . Thymidine analogues for tracking DNA synthesis. Molecules 2011; 9: 7980–7993.

    Article  Google Scholar 

  23. Shan-Cheng R, Min Q, Ying-Hao S . Investigating intratumour heterogeneity by single-cell sequencing. Asian J Androl 2013; 15: 729–734.

    Article  Google Scholar 

  24. Junttila MR, de Sauvage FJ . Influence of tumour micro-environment heterogeneity on therapeutic response. Nature 2013; 501: 346–354.

    Article  CAS  Google Scholar 

  25. Gerlinger M, Rowan AJ, Horswell S, Larkin J, Endesfelder D, Gronroos E et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 2012; 10: 883–892.

    Article  Google Scholar 

  26. Al-Lazikani B, Banerji U, Workman P . Combinatorial drug therapy for cancer in the post-genomic era. Nat Biotechnol 2012; 7: 679–692.

    Article  Google Scholar 

  27. Natrajan R, Wilkerson P . From integrative genomics to therapeutic targets. Cancer Res 2013; 73: 3483–3488.

    Article  CAS  Google Scholar 

  28. McMillin DW, Delmore J, Weisberg E, Negri JM, Geer DC, Klippel S et al. Tumor cell-specific bioluminescence platform to identify stroma-induced changes to anticancer drug activity. Nat Med 2010; 16: 483–489.

    Article  CAS  Google Scholar 

  29. Pemovska T, Kontro M, Yadav B, Edgren H, Eldfors S, Szwajda A et al. Individualized systems medicine strategy to tailor treatments for patients with chemorefractory acute myeloid leukemia. Cancer Discov 2013; 3: 1416–1429.

    Article  CAS  Google Scholar 

  30. Centenera MM, Raj GV, Knudsen KE, Tilley WD, Butler LM . Ex vivo culture of human prostate tissue and drug development. Nat Rev Urol 2013; 10: 483–487.

    Article  CAS  Google Scholar 

  31. Lin D, Xue H, Wang Y, Wu R, Watahiki A, Dong X et al. Next generation patient-derived prostate cancer xenograft models. Asian J Androl 2014; 16: 407–412.

    Article  Google Scholar 

  32. Cleary AS, Leonard TL, Gestl SA, Gunther EJ . Tumour cell heterogeneity maintained by cooperating subclones in Wnt-driven mammary cancers. Nature 2014; 7494: 113–117.

    Article  Google Scholar 

  33. Hu Y, Smyth GK . ELDA: extreme limiting dilution analysis for comparing depleted and enriched populations in stem cell and other assays. J Immunol Methods 2009; 347: 70–78.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We appreciate the efforts of Dr Mrunalini Moghe (Deenanath Mangeshkar Hospital, Pune) and Dr Anjali Kusumbe in karyotyping, Mr Swapnil Kamble in cryosectioning and thank Dr Shona Nag (Jehangir Hospital, Pune) for providing gemcitabine. This research is supported by NCCS intra-mural grants to SAB.

Author Contributions

RRN performed experiments, analyzed data, assisted manuscript drafting and carried out reviewer suggested revisions; AKS performed epigenetic drug-associated experiments, analyzed data and assisted manuscript drafting; AMM assisted with in vivo experiments; MFK analyzed data for derivation of checker board representation of drug responses; and SAB conceived the project, planned and finalized experimental design, analyzed data, drafted and edited the manuscript and its revision.

Author information

Authors and Affiliations

  1. National Centre for Cell Science, NCCS Complex, Pune, India

    R R Naik, A K Singh, A M Mali, M F Khirade & S A Bapat

Authors
  1. R R Naik
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A K Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A M Mali
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. M F Khirade
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. S A Bapat
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S A Bapat.

Ethics declarations

Competing interests

Following patents have been filed relating to the findings of this manuscript, 173/MUM/2014, PCT/IB2015/050358, and Indian provisional patent application 2980/MUM/2014.

Additional information

Supplementary Information accompanies this paper on the Oncogene website

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Naik, R., Singh, A., Mali, A. et al. A tumor deconstruction platform identifies definitive end points in the evaluation of drug responses. Oncogene 35, 727–737 (2016). https://doi.org/10.1038/onc.2015.130

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/onc.2015.130

This article is cited by

Search

Advanced search

Quick links