Skip to main content

A Methodological Approach to Personalized Therapies in Metastatic Cancer

  • Chapter
  • First Online:

Part of the book series: The Tumor Microenvironment ((TTME,volume 3))

Abstract

Personalized medicine should consist of a methodological therapy approach. Therefore, metastatic tumors have to be rendered usable for innovative action-theoretical therapy approaches to generate therapy-relevant tumor models and to uncover novel patterns of targets.

A new therapeutic level could be accomplished by introducing a pragmatic ­communication theory based on clinical results from less toxic combined ­biomodulatory therapies, altering the validity and denotation of cellular biochemical processes. A post-genomic view expands the role of proteins as an element within a network of communicative interactions. In a more abstract way, proteins and cells can be expressed as systems objects, which acquire contextual functions within circumscriptive functional modules or within the holistic communicative network of a tumor system. Biomodulatory therapies allow access to modular systems features as well as to the discrepancies between the functionality of single cell systems within a tumor compartment and the site-specific systems requirements of an organ (rationalization).

This way, modular tumor architectures, rationalization processes, deformations, and the Achilles’ heels of tumor systems may be implemented into therapeutic considerations to expand therapy options by individual systems-relevant and ­stage-relevant features (secretome, molecular imaging). Multi-level ­decision-making during therapy, i.e. biomarker-guided selection of therapies for individual patients, consecutively necessitates novel trial designs.

Selection of patients for therapy could be replaced by selecting therapies for patients, corresponding to the stage-dependent developmental status of a tumor system in an individual patient.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Dang J, Hedayati A, Hampel K, Toklu C (2008) An ontological knowledge framework for adaptive medical workflow. J Biomed Inform 41: 829–836.

    Article  PubMed  Google Scholar 

  2. Hudson SE (2007) Biochemical informatics methods for diagnosis and disease management. Conference on the proceedings of IEEE Engineering in Medicine and Biology Society 2007, pp 3769–3772.

    Google Scholar 

  3. Reichle A (2009) Tumor systems need to be rendered usable for a new action theoretical abstraction: The starting point for novel therapeutic options. Curr Cancer Ther Rev 5: 232–242.

    Article  CAS  Google Scholar 

  4. Reichle A, Hildebrandt GC (2009) Principles of modular tumor therapy. Cancer Microenviron 2(Suppl 1):227–237.

    Article  PubMed  Google Scholar 

  5. Anderson AR, Quaranta V (2008) Integrative mathematical oncology. Nat Rev Cancer 8: 227–234.

    Article  PubMed  CAS  Google Scholar 

  6. Colmone A, Amorim M, Pontier AL, et al (2008) Leukemic cells create bone marrow niches that disrupt the behavior of normal hematopoietic progenitor cells. Science 322: 1861–1865.

    Article  PubMed  CAS  Google Scholar 

  7. Acharya CR, Hsu DS, Anders CK, et al (2008) Gene expression signatures, clinicopathological features, and individualized therapy in breast cancer. JAMA 299: 1574–1587.

    Article  PubMed  CAS  Google Scholar 

  8. Kohl P, Noble D (2009) Systems biology and the virtual physiological human. Mol Syst Biol 5: 292.

    Article  PubMed  Google Scholar 

  9. Noble D (2008) Genes and causation. Philos Transact A Math Phys Eng Sci 366: 3001–3015.

    Article  CAS  Google Scholar 

  10. Galon J, Costes A, Sanchez-Cabo F, et al (2006) Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science 313: 1960–1964.

    Article  PubMed  CAS  Google Scholar 

  11. Pliarchopoulou K, Pectasides D (2009) First-line chemotherapy of non-seminomatous germ cell tumors (NSGCTs). Cancer Treat Rev 35:563–569

    Article  PubMed  CAS  Google Scholar 

  12. Simon JA, Szankasi P, Nguyen DK, et al (2000) Differential toxicities of anticancer agents among DNA repair and checkpoint mutants of Saccharomyces cerevisiae. Cancer Res 60: 328–333.

    PubMed  CAS  Google Scholar 

  13. Klein CA (2009) Parallel progression of primary tumours and metastases. Nat Rev Cancer 9: 302–312.

    Article  PubMed  CAS  Google Scholar 

  14. Hait WN, Hambley TW (2009) Targeted cancer therapeutics. Cancer Res 69: 1263–1267.

    Article  PubMed  CAS  Google Scholar 

  15. Goldhirsch A, Ingle JN, Gelber RD, et al (2009) Thresholds for therapies: highlights of the St Gallen International Expert Consensus on the primary therapy of early breast cancer 2009. Ann Oncol 20: 1319–1329.

    Google Scholar 

  16. Krynetskiy E, Lee CI (2009) Introducing pharmacy students to pharmacogenomic analysis. Am J Pharm Educ 73: 71.

    Article  PubMed  Google Scholar 

  17. Reichle A, Vogt T (2008) Systems biology: a therapeutic target for tumor therapy. Cancer Microenviron 1: 159–170.

    Article  PubMed  Google Scholar 

  18. Mantovani A, Allavena P, Sica A, et al (2008) Cancer-related inflammation. Nature 454: 436–444.

    Article  PubMed  CAS  Google Scholar 

  19. Shanmugam M, McBrayer SK, Rosen ST (2009) Targeting the Warburg effect in hematological malignancies: from PET to therapy. Curr Opin Oncol 21:531–536

    Article  PubMed  Google Scholar 

  20. Milsom CC, Yu JL, Mackman N, et al (2008) Tissue factor regulation by epidermal growth factor receptor and epithelial-to-mesenchymal transitions: effect on tumor initiation and angiogenesis. Cancer Res 68: 10068–10076.

    Article  PubMed  CAS  Google Scholar 

  21. Chen Q, Zhang H, Li Q, et al (2009) Three promoters regulate tissue- and cell type-specific expression of murine interleukin-1 receptor type I. J Biol Chem 284: 8703–8713.

    Article  PubMed  CAS  Google Scholar 

  22. Podder S, Mukhopadhyay P, Ghosh TC (2009) Multifunctionality dominantly determines the rate of human housekeeping and tissue specific interacting protein evolution. Gene 439: 11–16.

    Article  PubMed  CAS  Google Scholar 

  23. Jones S, Zhang X, Parsons DW, et al (2008) Core signaling pathways in human pancreatic cancers revealed by global genomic analyses. Science 321: 1801–1806.

    Article  PubMed  CAS  Google Scholar 

  24. Vardiman JW (2009) Chronic myelogenous leukemia, BCR-ABL1+. Am J Clin Pathol 132: 250–260.

    Article  PubMed  CAS  Google Scholar 

  25. Hartwell LH, Hopfield JJ, Leibler et al (1999) From molecular to modular cell biology. Nature 402: C47–C52.

    Article  PubMed  CAS  Google Scholar 

  26. Chumakov PM (2007) Versatile functions of p53 protein in multicellular organisms. Biochemistry (Mosc.) 72: 1399–1421.

    Article  CAS  Google Scholar 

  27. Chew LJ, Gallo V (2008) The Yin and Yang of Sox proteins: activation and repression in development and disease. J. Neurosci. Res 87:3277–3287.

    Article  Google Scholar 

  28. Huminiecki L, Goldovsky L, Freilich S, et al (2009) Emergence, development and diversification of the TGF-beta signalling pathway within the animal kingdom. BMC Evol Biol 9: 28.

    Article  PubMed  Google Scholar 

  29. Mankan AK, Lawless MW, Gray SG, et al (2008) NF-kappaB Regulation: the Nuclear Response. J Cell Mol Med13: 631–643.

    Article  Google Scholar 

  30. Kim D, Kolch W, Cho KH (2009) Multiple roles of the NF-{kappa}B signaling pathway regulated by coupled negative feedback circuits. FASEB J 23:2796–2802.

    Article  PubMed  CAS  Google Scholar 

  31. Luscombe NM, Babu MM, Yu H, et al (2004) Genomic analysis of regulatory network dynamics reveals large topological changes. Nature 431: 308–312.

    Article  PubMed  CAS  Google Scholar 

  32. Zhang Z, Zhang J (2009) A big world inside small-world networks. PLoS One 4: e5686.

    Article  PubMed  Google Scholar 

  33. Mader RM (2006) Links between biology, prognosis and prediction of response to chemotherapy in colorectal cancer. Onkologie 29: 334–341.

    Article  PubMed  CAS  Google Scholar 

  34. Slamon DJ, Leyland-Jones B, Shak S, et al (2001) Use of chemotherapy plus a monoclonal antibody against HER2 for metastatic breast cancer that overexpresses HER2. N Engl J Med 344: 783–792.

    Article  PubMed  CAS  Google Scholar 

  35. Vogelstein B, Kinzler KW (2004) Cancer genes and the pathways they control. Nat Med 10: 789–799.

    Article  PubMed  CAS  Google Scholar 

  36. Bredel M, Scholtens DM, Harsh GR, et al (2009) A network model of a cooperative genetic landscape in brain tumors. JAMA 302: 261–275.

    Article  PubMed  CAS  Google Scholar 

  37. Raponi M, Dossey L, Jatkoe T, et al (2009) MicroRNA classifiers for predicting prognosis of squamous cell lung cancer. Cancer Res 69: 5776–5783.

    Article  PubMed  CAS  Google Scholar 

  38. Bargou R, Leo E, Zugmaier G, et al (2008) Tumor regression in cancer patients by very low doses of a T cell-engaging antibody. Science 321: 974–977.

    Article  PubMed  CAS  Google Scholar 

  39. Hecht JR, Mitchell E, Chidiac T, et al (2009) A randomized phase IIIB trial of chemotherapy, bevacizumab, and panitumumab compared with chemotherapy and bevacizumab alone for metastatic colorectal cancer. J Clin Oncol 27: 672–680.

    Article  PubMed  CAS  Google Scholar 

  40. Grothey A, Galanis E (2009) Targeting angiogenesis: progress with anti-VEGF treatment with large molecules. Nat. Rev Clin Oncol 2009.

    Google Scholar 

  41. Hafner C, Reichle A, Vogt T. (2005). Conventional therapeutics with antiangiogenic activity. In: Davis DW, Herbst RS, Abbruzzese JL (eds) Antiangiogenic cancer therapy. CRC Press. Curr Cancer Drug Targets 2008: 301–327.

    Google Scholar 

  42. Hafner C, Reichle A, Vogt T (2005) New indications for established drugs: combined tumor-stroma-targeted cancer therapy with PPARgamma agonists, COX-2 inhibitors, mTOR antagonists and metronomic chemotherapy. Curr Cancer Drug Targets 5: 393–419.

    Article  PubMed  CAS  Google Scholar 

  43. Pahler JC, Tazzyman S, Erez N, et al (2008) Plasticity in tumor-promoting inflammation: impairment of macrophage recruitment evokes a compensatory neutrophil response. Neoplasia 10: 329–340.

    PubMed  CAS  Google Scholar 

  44. Trosko JE (2006) From adult stem cells to cancer stem cells: Oct-4 Gene, cell-cell communication, and hormones during tumor promotion. Ann NY Acad Sci 1089: 36–58.

    Article  PubMed  CAS  Google Scholar 

  45. Kaipainen A, Kieran MW, Huang S, et al (2007) PPARalpha deficiency in inflammatory cells suppresses tumor growth. PLoS One 2: e260.

    Article  PubMed  Google Scholar 

  46. Hutchinson L, DeVita VT, Jr (2008) The era of personalized medicine: back to basics. Nat Clin Pract Oncol 5: 623.

    Article  PubMed  Google Scholar 

  47. Hellerstein MK (2008) Exploiting complexity and the robustness of network architecture for drug discovery. J Pharmacol Exp Ther 325: 1–9.

    Article  PubMed  CAS  Google Scholar 

  48. Anguiano A, Tuchman SA, Acharya C, et al (2009) Gene expression profiles of tumor biology provide a novel approach to prognosis and may guide the selection of therapeutic targets in multiple myeloma. J Clin Oncol 2009.

    Google Scholar 

  49. Bild AH, Parker JS, Gustafson AM, et al (2009) An integration of complementary strategies for gene-expression analysis to reveal novel therapeutic opportunities for breast cancer. Breast Cancer Res 11: R55.

    Article  PubMed  Google Scholar 

  50. Klein CA, Stoecklein NH (2009) Lessons from an aggressive cancer: evolutionary dynamics in esophageal carcinoma. Cancer Res 69: 5285–5288.

    Article  PubMed  CAS  Google Scholar 

  51. Yeatman TJ (2009) Predictive biomarkers: identification and verification. J Clin Oncol 27: 2743–2744.

    Article  PubMed  Google Scholar 

  52. Donovan MJ, Costa J, Cordon-Cardo C. (2009). Systems pathology: a paradigm shift in the practice of diagnostic and predictive pathology. Cancer 115: 3078–3084.

    Article  PubMed  Google Scholar 

  53. Rodig SJ, Ouyang J, Juszczynski P, et al (2008) AP1-dependent galectin-1 expression delineates classical hodgkin and anaplastic large cell lymphomas from other lymphoid malignancies with shared molecular features. Clin Cancer Res 14: 3338–3344.

    Article  PubMed  CAS  Google Scholar 

  54. Bacac M, Provero P, Mayran N, et al (2006) A mouse stromal response to tumor invasion predicts prostate and breast cancer patient survival. PLoS One 1: e32.

    Article  PubMed  Google Scholar 

  55. Meyer S, Vogt T, Landthaler M, et al (2009). Cyclooxygenase 2 (COX2) and peroxisome proliferator-activated receptor gamma (PPARG) are stage-dependent prognostic markers of malignant melanoma. PPAR Res 2009: 848645.

    PubMed  Google Scholar 

  56. Haudek VJ, Slany A, Gundacker NC, et al (2008). Proteome maps of the main human peripheral blood constituents. J Prot Res 8: 3834–3843

    Article  Google Scholar 

  57. Weber WA. (2009). Assessing tumor response to therapy. J Nucl Med 50 (Suppl 1): 1S–10S.

    Article  PubMed  CAS  Google Scholar 

  58. Kherlopian AR, Song T, Duan Q, Neimark MA, Po MJ, Gohagan JK, Laine AF. (2008). A review of imaging techniques for systems biology. BMC Syst Biol 2: 74.

    Article  PubMed  Google Scholar 

  59. Hawk ET, Matrisian LM, Nelson WG, Dorfman GS, Stevens L, Kwok J, Viner J, Hautala J, Grad O. (2008). The translational research working group developmental pathways: introduction and overview. Clin Cancer Res 14: 5664–5671.

    Article  PubMed  Google Scholar 

  60. Glinsky GV. (2008). “Stemness” genomics law governs clinical behavior of human cancer: implications for decision making in disease management. J Clin Oncol 26: 2846–2853.

    Article  PubMed  Google Scholar 

  61. Eberhard DA, Giaccone G, Johnson BE. (2008). Biomarkers of response to epidermal growth factor receptor inhibitors in Non-Small-Cell Lung Cancer Working Group: standardization for use in the clinical trial setting. J Clin Oncol 26: 983–994.

    Article  PubMed  Google Scholar 

  62. Lilenbaum R, Axelrod R, Thomas S, Dowlati A, Seigel L, Albert D, Witt K, Botkin D. (2008). Randomized phase II trial of erlotinib or standard chemotherapy in patients with advanced non-small-cell lung cancer and a performance status of 2. J Clin Oncol 26: 863–869.

    Article  PubMed  CAS  Google Scholar 

  63. Yang HM, Do HJ, Kim DK, Park JK, Chang WK, Chung HM, Choi SY, Kim JH. (2007). Transcriptional regulation of human Oct4 by steroidogenic factor-1. J Cell Biochem 101: 1198–1209.

    Article  PubMed  CAS  Google Scholar 

  64. Muruganandan S, Roman AA, Sinal CJ. (2009). Adipocyte differentiation of bone marrow-derived mesenchymal stem cells: cross talk with the osteoblastogenic program. Cell Mol Life Sci 66: 236–253.

    Article  PubMed  CAS  Google Scholar 

  65. Chearwae W, Bright JJ. (2008). PPARgamma agonists inhibit growth and expansion of CD133+ brain tumour stem cells. Br J Cancer 99: 2044–2053.

    Article  PubMed  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Albrecht Reichle .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer Netherlands

About this chapter

Cite this chapter

Reichle, A., Vogt, T., Hildebrandt, G.C. (2010). A Methodological Approach to Personalized Therapies in Metastatic Cancer. In: Reichle, A. (eds) From Molecular to Modular Tumor Therapy. The Tumor Microenvironment, vol 3. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-9531-2_25

Download citation

Publish with us

Policies and ethics