Abstract Rationale. Despite promising beginnings, molecular classifiers derived from statistical learning do not yet appear to be sufficiently mature for clinical use. Besides known limitations, the nearly universal absence of mechanistic underpinnings for such signatures represents as major barrier toward successful implementation of clinically useful biomarkers. To overcome this limitation we constrained the search for predictive models to those with mechanistic justification, by incorporating microRNA (miR) and transcription factor (TF) gene regulatory networks directly into the learning process of cancer phenotypes. Methods. To illustrate the impact of embedding such regulatory motifs into computational learning, we analyzed the ability to predict estrogen receptor (ER) status from transcriptional data. We applied this approach to two independent breast cancer studies used as training and validation sets respectively. This analysis provided a test case with well-characterized clinical attributes, in which the ER itself is a TF engaged in regulatory miR/TF motifs. We built our predictors using Top Scoring Pair (TSP), a two-gene parameter-free classifier returning one class (ER positive) or the other (ER negative) based on the relative ordering of the two genes. We compared classification performance between TSPs chosen from all possible gene pairs and TSPs constructed under network-based constraints - “random” and “mechanistic” TSPs respectively hereafter. Each “mechanistic” TSP consists of a gene pair: the first gene regulates a miR or a TF “hub”, which in turn regulates the second gene. We started from a network of 200 TFs, 373 miRs, and 2772 target genes based on regulatory information from the miRgen v2.0 and TarBase v5.0 databases. Results. We assessed the classification accuracy of the TSP classifiers derived from the training dataset in the validation set and nearly all top-performing predictors were based on regulatory motifs. A Wilcoxon rank-sum test comparing the “random” classifiers with either TF or miR based TSPs had P-values of 10−14 and 10−26, respectively. Most of such top “mechanistic” predictors involved the ER gene (ERS1), consistent with the underlying biology. The mechanistic predictor also paired ERS1 expression with genes relevant to the biology. For instance, TSP selected POU2F1 _ a TF member of the POU family also known as OCT1 _ which physically interacts with the ER itself and BRCA1, recruiting BRCA1 to the ESR1 promoter modulating ER expression. Consistent with the classifier, BRCA1-mutant breast tumors are typically estrogen ER negative. Conclusions. We have implemented a novel class of mechanistic predictors by ”hardwiring” gene regulatory network information into statistical learning of cancer phenotypes. This approach has intrinsic added value for knowledge discovery and disease treatment design, and will ultimately move the field towards a successful transition to personalized health care. Citation Format: Bahman Afsari, Elana Judith Fertig, Laurent Younes, Donald Geman, Luigi Marchionni. Hardwiring mechanism into predicting cancer phenotypes by computational learning. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 5342. doi:10.1158/1538-7445.AM2014-5342