Phylogenetic Analysis of Mass Spectrometry or Gene Array Data for the Diagnosis of Physiological Conditions

摘要

A universal data-mining platform capable of analyzing mass spectrometry (MS) serum proteomic profiles and/or gene array data to produce biologically meaningful classification; i.e., group together biologically related specimens into clades. This platform utilizes the principles of phylogenetics, such as parsimony, to reveal susceptibility to cancer development (or other physiological or pathophysiological conditions), diagnosis and typing of cancer, identifying stages of cancer, as well as post-treatment evaluation. To place specimens into their corresponding clade(s), the invention utilizes two algorithms: a new data-mining parsing algorithm, and a publicly available phylogenetic algorithm (MIX). By outgroup comparison (i.e., using a normal set as the standard reference), the parsing algorithm identifies under and/or overexpressed gene values or in the case of sera, (i) novel or (ii) vanished MS peaks, and peaks signifying (iii) up or (iv) down regulated proteins, and scores the variations as either derived (do not exit in the outgroup set) or ancestral (exist in the outgroup set); the derived is given a score of “1”, and the ancestral a score of “0”—these are called the polarized values. Furthermore, the shared derived characters that it identifies are potential biomarkers for cancers and other conditions and their subclasses.