We aim to provide clinically applicable, reproducible, mechanistic interpretations of gene expression changes that lack in gene overlap among predictive gene-signatures. Using a method we recently developed, Functional Analysis of Individual Microarray Expression (FAIME), we provide evidence that Gene Ontology-anchored signatures (GO-signatures) show reliable prognosis in lung cancer. In order to demonstrate the biological congruence and reproducibility of FAIME-derived mechanism classifiers, we chose a disease where gene expression classifiers signatures alone had failed to significantly stratify a larger collection of samples and that exhibited poor or no genetic overlap. For each patient in the two lung adenocarcinoma studies, personalized FAIME-profiles of GO biological processes are generated from genome-wide expression profiles. For both training studies, GO-signatures significantly associated to patient mortality were identified (Prediction Analysis for Microarrays; three-fold cross-validation). These two GO-signatures could effectively stratify patients from an independent validation cohort into sub-groups that show significant differences in disease-free survival (log-rank test P=0.019; P=0.001). Importantly, significant mechanism overlaps assessed by information-theory similarity were detected between the two GO-signatures (Fischer Exact Test p=0.001). Hence, together with machine learning technologies, FAIME could be utilized to develop an ontology-driven and expression-anchored prognostic signature that is personalized for an individual patient.