Room “Sala Seminari” - Abacus Building (U14)
Utilizing mechanism-centric regulatory network-based approaches
for biomarker discovery in oncology
Speaker
Prof. Antonina Mitrofanova
Associate Dean for Research, Rutgers School of Health Professions
Deputy Director, Rutgers Center for Biomedical Informatics and Health AI (BMIHAI)
Associate Professor, Department of Biomedical and Health Informatics
Newark, NJ, USA
Abstract
We have developed a novel computational algorithm TR-2-PATH that reconstructs first-of-its kind mechanism-centric regulatory network, which connects molecular pathways to their upstream transcriptional regulatory programs, and prioritizes them as markers of therapeutic resistance in cancer. Such network offers a novel way to identify biomarkers that are mechanisms-centric, rather than based on individual genes or alterations - a new way to identify functional interactions and valuable therapeutic targets. We have applied TR-2-PATH to reconstruct a mechanism-centric regulatory network for metastatic castration-resistant prostate cancer (mCRPC). Network mining step addressed a knowledge gap of multi-collinearity among upstream transcriptional regulators (TRs) and identified TR groups that collaborate to regulate downstream pathways. Interrogating this network with signatures of resistance to Enzalutamide, a second-generation androgen-deprivation drug commonly administered to mCRPC, identified a collaboration between NME2 TR program and MYC molecular pathways as a biomarker of primary resistance to Enzalutamide. In vitro and in vivo experimental validation confirmed cooperation of these mechanisms and demonstrated that their joined therapeutic targeting is not only effective to prevent resistance to Enzalutamide, but also re-sensitizes Enzalutamide resistant tumors in vivo, allowing Enzalutamide to work longer. We propose to use MYC and NME2 as markers to identify patients at risk of Enzalutamide resistance and as effective therapeutic targets for patients that failed Enzalutamide. Our novel algorithm is generalizable and could be applied to study a multitude of biologically and clinically important questions, including (but not limited to) therapeutic resistance, metastatic progression, metastatic site preference, tumor heterogeneity and plasticity across cancer types as well as in other diseases. We have been extending this method to include mRNA alternative splicing, non-coding regulation, and deep learning techniques.
Short Bio
Antonina Mitrofanova is an Associate Professor in the Department of Biomedical and Health Informatics, Associate Dean for Research at Rutgers Health, SHP and Deputy Director Rutgers Health Center for Biomedical Informatics and Health AI. She acquired her passion for medicine and oncology during 4 years in Medical School in Kyiv, Ukraine. In the US, she received her PhD in Computer Science from NYU Courant Institute of Mathematical Sciences, which was followed by her PostDoctoral training in Computational Cancer Biology at Columbia University Cancer Center. Antonina’s research is focused on developing novel computational algorithms to identify molecular (transcriptomic, genomic, epigenomic) mechanisms and markers of cancer progression and therapeutic response. Her work has been featured in Cancer Cell, Cell Reports, European Urology, Nature Cancer, The Lancet EBiomedicine, Nature Communications etc. Antonina has received funding from National Science Foundation (NSF), Prostate Cancer Foundation (PCF), American Cancer Society (ACS), NIH National Library of Medicine, and Department of Defense (DOD). She is a recipient of Innovate100 Leadership award from Innovate NJ and PECASE award from the US president Biden.
contact person for this Seminar: marco.antoniotti@unimib.it