Dr. Jing Tang

Associate Professor
University of Helsinki

Biosketch

Dr. Jing Tang is an associate professor of medical bioinformatics at the University of Helsinki. He received Ph.D. in statistics from the University of Helsinki. During the Ph.D. study, he developed Bayesian model-based clustering methods for identifying bacterial population structures, and further proposed a network model to estimate the inter-clustering genetic exchange. During his post-doc at the VTT Technical Research Center of Finland, he developed partial correlation networks to distinguish causality from associations when integrating multi-omics data. He is an awardee of the prestigious ERC Starting Grant 2016, focusing on informatics approaches to predict, understand, and test personalized drug combinations in cancer. All the methods are offered with open-source tools that life science and drug discovery researchers frequently use. Recently his team has won three international competitions (DREAM Challenges) for the prediction of drug targets, drug sensitivity, and immunotherapies.

Keynote Title & Abstract

Title: Network pharmacology approaches to predict, test and understand drug combinations in cancer
Precision medicine still faces a significant challenge in leveraging patient data to make the optimal treatment decisions, especially in cancer treatment. Cancer drug discovery has clearly shifted towards targeted therapies and immunotherapies. However, their success in clinical trials has been limited, largely because we don’t fully understand why patients respond differently to the same therapy. More perplexingly, durable drug responses are rare. Patients urgently need a combination of drugs that can more effectively inhibit the cancer cells and block drug resistance. We aim to accelerate the discovery of drug combination therapies using network-based computational approaches to (i) predict individualized drug combinations and pinpoint their therapeutic target interactions (ii) to evaluate the degree of synergy in the drug combination screens and (iii) to understand the mechanisms of drug combinations. First, I will introduce computational models to leverage functional genetic screen and molecular features in identifying more reliable drug targets for cancer cells. Secondly, I will describe mathematical models including the CSS and ZIP methods, together with a harmonized drug sensitivity data portal called DrugComb. Lastly, I will present case studies in breast cancer, ovarian cancer, and hematological cancer where our methods have identified promising combination therapies for further exploration.