TY - JOUR T1 - Molecular Inverse Comorbidity between Alzheimer’s disease and Lung Cancer: new insights from Matrix Factorization JF - bioRxiv DO - 10.1101/643890 SP - 643890 AU - Alessandro Greco AU - Jon Sanchez Valle AU - Vera Pancaldi AU - Anaïs Baudot AU - Emmanuel Barillot AU - Michele Caselle AU - Alfonso Valencia AU - Andrei Zinovyev AU - Laura Cantini Y1 - 2019/01/01 UR - http://biorxiv.org/content/early/2019/05/21/643890.abstract N2 - Matrix Factorization (MF) is an established paradigm for large-scale biological data analysis with tremendous potential in computational biology.We here challenge MF in depicting the molecular bases of epidemiologically described Disease-Disease (DD) relationships. As use case, we focus on the inverse comorbidity association between Alzheimer’s disease (AD) and lung cancer (LC), described as a lower than expected probability of developing LC in AD patients. To the day, the molecular mechanisms underlying DD relationships remain poorly explained and their better characterization might offer unprecedented clinical opportunities.To this goal, we extend our previously designed MF-based framework for the molecular characterization of DD relationships. Considering AD-LC inverse comorbidity as a case study, we highlight multiple molecular mechanisms, among which the previously identified immune system and mitochondrial metabolism. We then discriminate mechanisms specific to LC from those shared with other cancers through a pancancer analysis. Additionally, new candidate molecular players, such as Estrogen Receptor (ER), CDH1 and HDAC, are pinpointed as factors that might underlie the inverse relationship, opening the way to new investigations. Finally, some lung cancer subtype-specific factors are also detected, suggesting the existence of heterogeneity across patients also in the context of inverse comorbidity.ADAlzheimer’s diseaseLCLung CancerRBHReciprocal Best HitDDDisease-Disease ER -