Mateo, F. | bioRxiv Science

bioRxiv2021

Mammary lineage dictates homologous recombination repair and PARP inhibitor vulnerability

It has long been assumed that all normal cells have the same capacity to engage homologous recombination (HR) and non-homologous end joining (NHEJ) to repair DNA double-strand breaks (DSBs), a concept exploited for DNA-damaging chemotherapeutics. We show that mammary epithelial lineage dictates the DSB repair pathway choice. Primary mammary proteomes and DSB repair enumeration by {gamma}-H2AX, Rad51 and DNA-PKc foci reveal that NHEJ operates in all epithelial cells, but high-fidelity HR is restricted to the luminal lineage. This translates to divergent poly (ADP-ribose) polymerase inhibitor (PARPi) vulnerability of mammary epithelial progenitor activity in both mouse and human, irrespective of the BRCA1/2 status. Proteome-defined lineage-specific signatures correlate to breast cancer subtypes and predict PARPi response of triple-negative human breast cancer xenografts. These intrinsically divergent HR characteristics of mammary cell types underpin a new strategy for identifying PARPi responders.

cancer biology↗

bioRxiv2018

Differential metabolic activity and discovery of therapeutic targets using summarized metabolic pathway models

Backgroundin spite of the increasing availability of genomic and transcriptomic data, there is still a gap between the detection of perturbations in gene expression and the understanding of their contribution to the molecular mechanisms that ultimately account for the phenotype studied. Alterations in the metabolism are behind the initiation and progression of many diseases, including cancer. The wealth of available knowledge on metabolic processes can therefore be used to derive mechanistic models that link gene expression perturbations to changes in metabolic activity that provide relevant clues on molecular mechanisms of disease and drug modes of action (MoA). In particular, pathway modules, which recapitulate the main aspects of metabolism, are especially suitable for this type of modeling.\n\nResultswe present Metabolizer, a web-based application that offers an intuitive, easy-to-use interactive interface to analyze differences in pathway module metabolic activities that can also be used for class prediction and in silico prediction of Knock-Out (KO) effects. Moreover, Metabolizer can automatically predict the optimal KO intervention for restoring a diseased phenotype. We provide different types of validations of some of the predictions made by Metabolizer.\n\nConclusionsMetabolizer is a web tool that allows understanding molecular mechanisms of disease or the MoA of drugs within the context of the metabolism by using gene expression measurements. In addition, this tool automatically suggests potential therapeutic targets for individualized therapeutic interventions.\n\nMetabolizer can be found at: http://metabolizer.babelomics.org.

bioinformatics↗