Autoantibody recognition mechanisms of p53 epitopes

TL;DR

This paper explores bioinformatic methods to identify sensitive p53 epitopes for early cancer detection, focusing on autoantibody recognition and cross-species epitope analysis to improve diagnostic sensitivity.

Contribution

It introduces a bioinformatic fractal scaling approach to identify promising p53 epitopes, including cross-species conserved 7mers, for early cancer detection.

Findings

p53 15mer epitopes are highly sensitive biomarkers for colon cancer

Fractal scaling analysis can identify sensitive epitopes from p53 sequence

Cross-species conserved 7mers may enhance detection of aggressive cancers

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 recent study of 50,000 patients (Pedersen et al., 2013) showed p53 15mer 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 bioinformatic fractal scaling analysis for identifying sensitive epitopes from the p53 amino acid sequence, and show how it could be used for early cancer detection (ECD). We trim 15mers to 7mers, and identify specific 7mers from other species that could be more sensitive to aggressive human cancers, such as liver cancer.