Biology subjects

Lien, J.

Publications and source records attributed to Lien, J..

2 recordsLinked to original sources

bioRxiv2025

Organoid-evaluable clinical biomarkers predict drug responses and guide new breast cancer therapies

Poor therapeutic response in subsets of breast cancer (BC) patients poses an ongoing challenge. Here, we present a biomarker-guided characterization of 44 patient-derived BC organoids, with the aim of modeling resistant disease with greater fidelity and developing an in-vitro system grounded in clinical data for testing alternative treatment strategies. We utilized patient transcriptomic and outcome data from the I-SPY2 clinical trial to develop predictive models of response to a range of therapies, using only organoid-detectable biomarkers as input. A model predicting response to veliparib-platinum chemotherapy (VP) in triple-negative BC (TNBC) was validated in organoids, showing that in vitro drug responses matched predictions from the patient data-derived model. A drug screen in VP-resistant TNBC organoids identified combination treatments that overcame resistance to cisplatin, including pro-apoptotic therapies. This demonstrates that gene expression-based resistance models derived from patient data can be successfully modeled in organoids that can then be used for therapeutic evaluation.

cancer biology↗

bioRxiv2019

Improved Automatic Pharmacovigilance: An Enhancement to the MedWatcher Social System for Monitoring Adverse Events

Traditional pharmacovigilance systems rely on adverse event reports received by regulatory authorities such as the United States Food and Drug Administration (FDA). These traditional systems suffer from underreporting and are not timely due to their reliance on third-party sentinels. To address these issues, the MedWatcher Social system for monitoring adverse events through automated processing of digital social media data and crowdsourcing was launched in 2012 by Boston Childrens Hospital and the FDA. The system is rooted in the well-established FDA MedWatch system.\n\nMedWatcher Social uses an indicator score approach to identify adverse events. This study evaluates the MedWatcher Social adverse event classifiers performance on Twitter data and proposes an enhancement to the indicator score method that results in improved adverse event identification.\n\nOur research suggests that automatic pharmacovigilance systems using the original indicator score approach should be updated. Careful consideration of modeling assumptions is critical when designing algorithms for computational epidemiology, and algorithms should be regularly reevaluated to identify enhancements and to remedy concept drift.

bioinformatics↗