Identification of a 5-Protein Biomarker Molecular Signature for Predicting Alzheimer’s Disease Martı´n Go´ mez Ravetti*, Pablo Moscato* Centre for Bioinformatics, Biomarker Discovery & Information-Based Medicine, The University of Newcastle, Callaghan, Australia Abstract Background: Alzheimer’s disease (AD) is a progressive brain disease with a huge cost to human lives. The impact of the disease is also a growing concern for the governments of developing countries, in particular due to the increasingly high number of elderly citizens at risk. Alzheimer’s is the most common form of dementia, a common term for memory loss and other cognitive impairments. There is no current cure for AD, but there are drug and non-drug based approaches for its treatment. In general the drug-treatments are directed at slowing the progression of symptoms. They have proved to be effective in a large group of patients but success is directly correlated with identifying the disease carriers at its early stages. This justifies the need for timely and accurate forms of diagnosis via molecular means. We report here a 5-protein biomarker molecular signature that achieves, on average, a 96% total accuracy in predicting clinical AD. The signature is composed of the abundances of IL-1a, IL-3, EGF, TNF-a and G-CSF. Methodology/Principal Findings: Our results are based on a recent molecular dataset that has attracted worldwide attention. Our paper illustrates that improved results can be obtained with the abundance of only five proteins. Our methodology consisted of the application of an integrative data analysis method. This four step process included: a) abundance quantization, b) feature selection, c) literature analysis, d) selection of a classifier algorithm which is independent of the feature selection process. These steps were performed without using any sample of the test datasets. For the first two steps, we used the application of Fayyad and Irani’s discretization algorithm for selection and quantization, which in turn creates an instance of the (alpha-beta)-k-Feature Set problem; a numerical solution of this problem led to the selection of only 10 proteins. Conclusions/Significance: the previous study has provided an extremely useful dataset for the identification of AD biomarkers. However, our subsequent analysis also revealed several important facts worth reporting: 1. A 5-protein signature (which is a subset of the 18-protein signature of Ray et al.) has the same overall performance (when using the same classifier). 2. Using more than 20 different classifiers available in the widely-used Weka software package, our 5- protein signature has, on average, a smaller prediction error indicating the independence of the classifier and the robustness of this set of biomarkers (i.e. 96% accuracy when predicting AD against non-demented control). 3. Using very simple classifiers, like Simple Logistic or Logistic Model Trees, we have achieved the following results on 92 samples: 100 percent success to predict Alzheimer’s Disease and 92 percent to predict Non Demented Control on the AD dataset. Citation: Go´ mez Ravetti M, Moscato P (2008) Identification of a 5-Protein Biomarker Molecular Signature for Predicting Alzheimer’s Disease. PLoS ONE 3(9): e3111. doi:10.1371/journal.pone.0003111 Editor: Joseph El Khoury, Massachusetts General Hospital and Harvard Medical School, United States of America Received April 21, 2008; Accepted August 4, 2008; Published September 3, 2008 Copyright: 2008 Gomez Ravetti, Moscato. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: The authors have no support or funding to report. Competing Interests: The authors have declared that no competing interests exist. * E-mail: martin.ravetti@newcastle.edu.au (MGR); pablo.moscato@newcastle.edu.au (PM) Introduction Recently, Ray et al. [1] made a significant contribution to the quest of finding a superior molecular test for an earlier diagnosis of Alzheimer’s disease (AD). The method appears to have signifi- cantly improved on the state-of-the-art and, as a consequence, their results attracted immediate worldwide attention. Using the abundance of 120 signalling proteins on a training set of 83 archived plasma samples, they produced an 18-protein signature. On two separate test sets of 92 (‘‘AD’’ Alzheimer’s samples against control) and 47 (‘‘MCI’’ mild cognitive impairment samples) the signature was able to show an overall effectiveness of 81% and 91% for AD predictability. We started this project by analysing the dataset made available and we are glad to report that we have been able to perfectly reproduce their mathematical methods and results from the available datasets. However, our subsequent analysis also pro- duced several important facts worth reporting: using an integrative bioinformatics approach, we identified a 6-protein signature that halves the number of errors in prediction of the previously proposed signature (on the ‘‘AD’’ dataset.), when using the same classifier (PAM). A 5-protein signature (which is a subset of the 18- protein signature of Ray et al.) has the same overall performance. Finally, using more than 20 different classifiers available in the widely-used Weka software package [2], our 5-protein signature has, on average, a smaller prediction error indicating the PLoS ONE | www.plosone.org 1 September 2008 | Volume 3 | Issue 9 | e3111