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Wednesday, 19th, May 2021
From 15h To 16h
Centre de recherche - Paris - Webinar

Enter the matrix: modeling tumor cell and immune cell interactions at the single cell resolution

Elana J. Fertig, PhD

Tumors employ complex, multi-scale cellular and molecular interactions that evolve over the course of therapeutic response. The changes in these pathways enables tumors to overcome therapeutic regimens, and ultimately acquire resistance. New molecular profiling technologies, including notably single cell technologies, provide an unprecedented opportunity to characterize these molecular relationships. However, interpreting the specific cellular and molecular pathways in therapeutic response requires complementary computational analysis methods. We developed an unsupervised learning method, CoGAPS, that employs Bayesian non-negative matrix factorization to disentangle distinct biological processes from high-throughput molecular data. Notably, this algorithm discovers dynamic compensatory signaling in acquired therapeutic resistance from time course bulk RNA-seq data and novel NK cell activation in anti-CTLA4 response from post-treatment scRNA-seq data. To further demonstrate that the inferred pathways are biological rather than computational artifacts, we developed a complementary transfer learning method to relate learned patterns between datasets. We demonstrate that this approach identifies robust molecular processes between model systems and human tumors and enables multi-platform data integration to delineate the drivers of therapeutic response and resistance.


Dr. Elana J. FERTIG
co-Director of the Single Cell Consortium Associate Director of the Convergence Institute Assistant Director of the Research Program in Quantitative Science Associate Professor of Oncology, Biomedical Engineering, and Applied Mathematics and Statistics

Johns Hopkins University

Invited by

SYSBIO Team Senior Scientist

Institut Curie

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Dr. Fertig runs an NCI funded hybrid computational and experimental lab in the systems biology of cancer and therapeutic response.  Her wet lab develops time course models of therapeutic resistance and performs single cell technology development. Her computational methods blend mathematical modeling and artificial intelligence to determine the biomarkers and molecular mechanisms of therapeutic resistance from multi-platform genomics data. These techniques have broad applicability beyond her resistance models, including notably to the analysis of clinical biospecimens, developmental biology, and neuroscience. 

Dr. Fertig is an Associate Professor of Oncology and Assistant Director of the Research Program in Quantitative Sciences and Associate Director of the Convergence Institute at Johns Hopkins University Sidney Kimmel Comprehensive Cancer Center, with secondary appointments in Biomedical Engineering and Applied Mathematics and Statistics, affiliations in the Institute of Computational Medicine, Center for Computational Genomics, Machine Learning, Mathematical Institute for Data Science, and the Center for Computational Biology. Prior to entering the field of computational cancer biology, Dr Fertig was a NASA research fellow in numerical weather prediction.  Dr. Fertig's research is featured in over eighty peer-reviewed publications, R/Bioconductor packages, and competitive funding portfolio as PI and co-I.  Notably, she led the team that won the HPN-DREAM8 algorithm to predict phospho-proteomic trajectories from therapeutic response in cancer cells. She serves on the editorial boards of the pre-eminent computational biology journals PLoS Computational Biology, Cell Systems, ImmunoInformatics, and as a study section member for the NCI Systems Biology, NCI Informatics Technology for Cancer Research, and Israel Cancer Research Fund Study Sections.