Cancer systems biology

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Cancer systems biology encompasses the application of systems biology approaches to cancer research, in order to study the disease as a complex adaptive system with emerging properties at multiple biological scales.[1][2][3] More explicitly, because cancer spans multiple biological, spatial and temporal scales, communication and feedback mechanisms across the scales create a highly complex dynamic system. The relationships between scales is not simple or necessarily direct, and sometimes become combinatorial, so that systems approaches are essential to evaluate these relationships quantitatively and qualitatively.

Cancer systems biology therefore adopts a holistic view of cancer[4] aimed at integrating its many biological scales, including genetics, signaling networks,[5] epigenetics,[6] cellular behavior, histology, (pre)clinical manifestations and epidemiology. Ultimately, cancer properties at one scale, e.g., histology, are explained by properties at a scale below, e.g., cell behavior. Likewise, a higher scale, e.g., epidemiology, can encroach on a lower scale, e.g., genetics. The fundamental concept is that percolation of properties across scales must be measured and taken into account in order to fully understand etiology, progression and dynamics of cancer. The systems biology approach relies heavily on the successes of decades of reductionism, which has clarified the component parts and mechanistic principles of living organisms, as well as their key alterations in cancer, especially at the genetic/genomic scale, to deep detail. Basic researchers and clinicians have progressively recognized the complexity of cancer and of its interaction with the micro- and macro-environment, since putting together the components to provide a cohesive view of the disease has been challenging and hampered progress. Cancer Systems Biology transcends the “reductionist” approach to cancer that typically produces causative explanations focused on a single gene or mutation, with little emphasis on inter-scale relationships.

Cancer systems biology merges traditional basic and clinical cancer research with “exact” sciences, such as applied mathematics, engineering, and physics. It incorporates a spectrum of “omics” technologies (genomics, proteomics, epigenomics, etc.) and molecular imaging, to generate computational algorithms and quantitative models[7] that shed light on mechanisms underlying the cancer process and predict response to intervention.


Cancer systems biology finds its roots in a number of events and realizations in biomedical research, as well as in technological advances. Historically cancer was identified, understood, and treated as a monolithic disease. It was seen as a “foreign” component that grew as a homogenous mass, and was to be best treated by excision. Besides the continued impact of surgical intervention, this simplistic view of cancer has drastically evolved. In parallel with the exploits of molecular biology, cancer research focused on the identification of critical oncogenes or tumor suppressor genes in the etiology of cancer. These breakthroughs revolutionized our understanding of molecular events driving cancer progression. Targeted therapy may be considered the current pinnacle of advances spawned by such insights.

Despite these advances, many unresolved challenges remain, including the dearth of new treatment avenues for many cancer types, or the unexplained treatment failures and inevitable relapse in cancer types where targeted treatment exists.[8] Such mismatch between clinical results and the massive amounts of data acquired by omics technology highlights the existence of basic gaps in our knowledge of cancer fundamentals. Cancer Systems Biology is steadily improving our ability to organize information on cancer, in order to fill these gaps. Key developments include:

  • The generation of comprehensive molecular datasets (genome, transcriptome, epigenomics, proteome, metabolome, etc.)
  • The Cancer Genome Atlas data collection[9]
  • Computational algorithms to extract drivers of cancer progression from existing datasets[10]
  • Statistical and mechanistic modeling of signaling networks[11]
  • Quantitative modeling of cancer evolutionary processes[4]
  • Mathematical modeling of cancer cell population growth[12]
  • Mathematical modeling of cellular responses to therapeutic intervention[13]
  • Mathematical modeling of cancer metabolism[7]

The practice of Cancer Systems Biology requires close physical integration between scientists with diverse backgrounds. Critical large-scale efforts are also underway to train a new workforce fluent in both the languages of biology and applied mathematics. At the translational level, Cancer Systems Biology should engender precision medicine application to cancer treatment.

National funding efforts[edit]

In 2004, the US National Cancer Institute launched a program effort on Integrative Cancer Systems Biology[14] to establish Centers for Cancer Systems Biology that focus on the analysis of cancer as a complex biological system. The integration of experimental biology with mathematical modeling will result in new insights in the biology and new approaches to the management of cancer. The program brings clinical and basic cancer researchers together with researchers from mathematics, physics, engineering, information technology, imaging sciences, and computer science to work on unraveling fundamental questions in the biology of cancer.[15]

See also[edit]


  1. ^ Wang, Edwin. Cancer Systems Biology. Chapman & Hall, 2010
  2. ^ Liu & Lauffenburger. Systems Biomedicine: Concepts and Perspectives. Academic Press, 2009.
  3. ^ Barillot, Emmanuel; Calzone, Laurence; Hupe, Philippe; Vert, Jean-Philippe; Zinovyev, Andrei (2012). Computational Systems Biology of Cancer. Chapman & Hall/CRC Mathematical & Computational Biology. p. 461. ISBN 978-1439831441. 
  4. ^ a b Anderson, AR; Quaranta (2008). "Integrative mathematical oncology". Nat Rev Cancer. 8 (3): 227–234. PMID 18273038. doi:10.1038/nrc2329. 
  5. ^ Kreeger, PK; Lauffenburger (2010). "Cancer systems biology: A network modeling perspective". Carcinogenesis. 31 (1): 2–8. PMC 2802670Freely accessible. PMID 19861649. doi:10.1093/carcin/bgp261. 
  6. ^ Huang, YW; Kuo, Stoner; Huang, Wang (2011). "An overview of epigenetics and chemoprevention". FEBS Lett. 585 (13): 2129–2136. PMC 3071863Freely accessible. PMID 21056563. doi:10.1016/j.febslet.2010.11.002. 
  7. ^ a b Lewis, NE; Abdel-Haleem, AM (2013). "The evolution of genome-scale models of cancer metabolism". Front. Physiol. 4: 237. doi:10.3389/fphys.2013.00237. 
  8. ^ Garraway; Jänne (2012). "Circumventing cancer drug resistance in the era of personalized medicine". Cancer Discovery. 2 (3): 214–226. PMID 22585993. doi:10.1158/2159-8290.CD-12-0012. 
  9. ^ Collins; Barker (2007). "Mapping the cancer genome. Pinpointing the genes involved in cancer will help chart a new course across the complex landscape of human malignancies". Sci Am. 296 (3): 50–57. PMID 17348159. 
  10. ^ Pe'er, Dana; Nir Hacohen (2011). "Principles and Strategies for Developing Network Models in Cancer". Cell. 144: 864–873. PMC 3082135Freely accessible. PMID 21414479. doi:10.1016/j.cell.2011.03.001. 
  11. ^ Tyson, J.J.; Baumann, W.T.; Chen, C.; Verdugo, A.; Tavassoly, I.; Wang, Y.; Weiner, L.M.; Clarke, R. (2011). "Dynamic modelling of oestrogen signalling and cell fate in breast cancer cells". Nat. Rev. Cancer. 11: 523–532. PMC 3294292Freely accessible. PMID 21677677. doi:10.1038/nrc3081. 
  12. ^ Tyson, D.R.; Garbett, S.P.; Frick, P.L.; Quaranta, V (2012). "Fractional proliferation: a method to deconvolve cell population dynamics from single-cell data". Nat. Methods. 9 (9): 923–928. PMC 3459330Freely accessible. PMID 22886092. doi:10.1038/nmeth.2138. 
  13. ^ Traina, Tiffany A.; U. Dugan; B. Higgins; K. Kolinksy; M. Theodoulou; C. A. Hudis; Larry Norton (2010). "Optimizing Chemotherapy Dose and Schedule by Norton-Simon Mathematical Modeling". Breast Disease. 31 (1): 7–18. doi:10.3233/BD-2009-0290. 
  14. ^ NCI Cancer Bulletin. Feb 24, 2004. V1, 8. p5-6
  15. ^ Gentles; Gallahan (2011). "Systems biology: confronting the complexity of cancer". Cancer Res. 71 (18): 5961–5964. PMC 3174325Freely accessible. PMID 21896642. doi:10.1158/0008-5472.CAN-11-1569.