Towards Organoid-Driven Cancer Precision Medicine
Precision Medicine has traditionally focused on genome sequencing to match patients to the most likely effective treatment. This approach, while increasingly effective, is nonetheless limited by our imperfect understanding of the genome and its mutations. To address this limitation, Dr. Elemento has initiated a wide-ranging, pan-cancer tumor avatar program levering tumor organoid ex vivo culture and state-of-the-art high-throughput drug screening. Dr. Elemento’s work seeks to inform therapy selection using empirical drug testing, including drug combinations. Dr. Elemento’s team is also actively exploring a variety of technological improvements for organoid technology from CRISPR screening, to immune co-cultures and new matrices. This seminar will provide a comprehensive overview of Dr. Elemento’s research program, discuss opportunities and challenges in the use of organoids for precision medicine, and describe the long-term vision for this exciting field.
Olivier Elemento, Ph.D., is Director of the Englander Institute for Precision Medicine at Weill Cornell Medicine. Additionally, Dr. Elemento serves as Associate Director of the Institute for Computational Biomedicine and guides students not only as Professor of Physiology and Biophysics, but also Computational Genomics in Computational Biomedicine.
Dr. Elemento’s lab combines Big Data analytics with experimentation to develop new ways to help prevent, diagnose, understand, treat, and cure cancer. Dr. Elemento’sresearch involves routine use of ultrafast DNA sequencing, proteomics, high-performance computing, mathematical modeling, and artificial intelligence/machine learning.
Dr. Elemento’s areas of focus include: systems biology of regulatory networks in normal and malignant cells, with a strong focus on blood cancers (lymphomas and leukemias); cancer genomics and Precision Medicine using novel computational algorithms to identify new cancer mutations to better understand why and where cancer mutations occur, with specific interest in whether 3D chromatin architecture predicts where mutations are more likely to occur; cancer epigenetics, using high-throughput experimental approaches and pattern detection techniques to investigate what mutated cancer genes do and the genome-wide epigenomic patterns they mediate; the evolution of tumor genomes, using high-throughput sequencing to investigate how the tumor genome and epigenome change with drug treatments; machine learning to detect cancer as early as possible to guide treatment accordingly; and the development of innovative computational approaches for analysis of high-throughput data sets (for example, metabolomics, proteomics, and high-throughput sequencing) obtained from cancer cells.