Adaptation and learning of molecular networks as a description of cancer development at the systems-level: potential use in anti-cancer therapies

Semin Cancer Biol. 2013 Aug;23(4):262-9. doi: 10.1016/j.semcancer.2013.06.005. Epub 2013 Jun 21.

Abstract

There is a widening recognition that cancer cells are products of complex developmental processes. Carcinogenesis and metastasis formation are increasingly described as systems-level, network phenomena. Here we propose that malignant transformation is a two-phase process, where an initial increase of system plasticity is followed by a decrease of plasticity at late stages of carcinogenesis as a model of cellular learning. We describe the hallmarks of increased system plasticity of early, tumor initiating cells, such as increased noise, entropy, conformational and phenotypic plasticity, physical deformability, cell heterogeneity and network rearrangements. Finally, we argue that the large structural changes of molecular networks during cancer development necessitate a rather different targeting strategy in early and late phase of carcinogenesis. Plastic networks of early phase cancer development need a central hit, while rigid networks of late stage primary tumors or established metastases should be attacked by the network influence strategy, such as by edgetic, multi-target, or allo-network drugs. Cancer stem cells need special diagnosis and targeting, since their dormant and rapidly proliferating forms may have more rigid, or more plastic networks, respectively. The extremely high ability of cancer stem cells to change the rigidity/plasticity of their networks may be their key hallmark. The application of early stage-optimized anti-cancer drugs to late-stage patients may be a reason of many failures in anti-cancer therapies. Our hypotheses presented here underlie the need for patient-specific multi-target therapies applying the correct ratio of central hits and network influences - in an optimized sequence.

Keywords: Adaptation; Anti-cancer therapies; B-Raf protein; BRAF; BRD4; CDK6; Cancer attractors; Cancer development; ERBB1; ERG; ERK; ETS-family oncogenic transcription factor; Epithelial–mesenchymal transition; FBJ murine osteosarcoma viral oncogene homolog; FOS; FOXO3A; IRS1; Interactome; MMP2; MYC; NES; Networks; PDGFR; PI3K; PKM2; RAS; RAS-homolog gene family member A; RHOA; Signaling; TGFBR; TNC; TP53 tumor suppressor protein; bromodomain-containing protein 4; cyclin-dependent kinase 6; epidermal growth factor receptor; extracellular signal regulated protein kinase; forkhead family transcription factor; insulin receptor substrate 1; mTORC1; mammalian target of rapamycin complex 1; matrix metalloproteinase 2; myelocytomatosis viral oncogene homolog protein; nestin intermediate filament protein; p53; phosphatidyl-inositol-3′-kinase; platelet-derived growth factor receptor; pyruvate kinase M2 isoform; small GTPase protein; tenascin C protein; transforming growth factor-β receptor.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Antineoplastic Agents / therapeutic use
  • Cell Transformation, Neoplastic / drug effects
  • Cell Transformation, Neoplastic / metabolism*
  • Cell Transformation, Neoplastic / pathology
  • Humans
  • Metabolic Networks and Pathways / drug effects
  • Models, Biological
  • Neoplasms / drug therapy
  • Neoplasms / metabolism*
  • Neoplasms / pathology
  • Neoplastic Stem Cells / drug effects
  • Neoplastic Stem Cells / metabolism*
  • Neoplastic Stem Cells / pathology
  • Signal Transduction*

Substances

  • Antineoplastic Agents