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Autoantibody recognition mechanisms of p53 epitopes

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  • Phillips, J.C.

Abstract

There is an urgent need for economical blood based, noninvasive molecular biomarkers to assist in the detection and diagnosis of cancers in a cost-effective manner at an early stage, when curative interventions are still possible. Serum autoantibodies are attractive biomarkers for early cancer detection, but their development has been hindered by the punctuated genetic nature of the ten million known cancer mutations. A landmark study of 50,000 patients (Pedersen et al., 2013) showed that a few p53 15-mer epitopes are much more sensitive colon cancer biomarkers than p53, which in turn is a more sensitive cancer biomarker than any other protein. The function of p53 as a nearly universal “tumor suppressor” is well established, because of its strong immunogenicity in terms of not only antibody recruitment, but also stimulation of autoantibodies. Here we examine dimensionally compressed bioinformatic fractal scaling analysis for identifying the few sensitive epitopes from the p53 amino acid sequence, and show how it could be used for early cancer detection (ECD). We trim 15-mers to 7-mers, and identify specific 7-mers from other species that could be more sensitive to aggressive human cancers, such as liver cancer. Our results could provide a roadmap for ECD.

Suggested Citation

  • Phillips, J.C., 2016. "Autoantibody recognition mechanisms of p53 epitopes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 162-170.
    • Handle: RePEc:eee:phsmap:v:451:y:2016:i:c:p:162-170
    DOI: 10.1016/j.physa.2016.01.021
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    References listed on IDEAS

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    1. Phillips, J.C., 2014. "Fractals and self-organized criticality in anti-inflammatory drugs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 415(C), pages 538-543.
    2. Bert Vogelstein & David Lane & Arnold J. Levine, 2000. "Surfing the p53 network," Nature, Nature, vol. 408(6810), pages 307-310, November.
    3. Phillips, J.C., 2014. "Fractals and self-organized criticality in proteins," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 415(C), pages 440-448.
    4. Michael S. Lawrence & Petar Stojanov & Craig H. Mermel & James T. Robinson & Levi A. Garraway & Todd R. Golub & Matthew Meyerson & Stacey B. Gabriel & Eric S. Lander & Gad Getz, 2014. "Discovery and saturation analysis of cancer genes across 21 tumour types," Nature, Nature, vol. 505(7484), pages 495-501, January.
    5. Fabrizio Chiti & Massimo Stefani & Niccolò Taddei & Giampietro Ramponi & Christopher M. Dobson, 2003. "Rationalization of the effects of mutations on peptide andprotein aggregation rates," Nature, Nature, vol. 424(6950), pages 805-808, August.
    6. Phillips, J.C., 2015. "Phase transitions in the web of science," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 428(C), pages 173-177.
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    Cited by:

    1. Phillips, J.C., 2017. "Autoantibody recognition mechanisms of MUC1," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 244-249.
    2. Phillips, J.C., 2017. "Hidden thermodynamic information in protein amino acid mutation tables," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 676-680.
    3. Phillips, J.C., 2017. "Giant hub Src and Syk tyrosine kinase thermodynamic profiles recapitulate evolution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 483(C), pages 330-336.

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