Systems Insights into Molecular Variability and Divergent Cancer Phenotypes

Understanding how heterogeneity arises in cells of similar origin within a uniform environment is a major challenge in biology. In the context of cancer, identifying the processes that lead to tumor heterogeneity is crucial, as it significantly impacts tumor progression and treatment resistance. In this presentation, I will explore the mechanistic details behind the origins and consequences of heterogeneity in cancer using experimental, bioinformatics, and computational methods. In the first example, I will discuss the emergence of a multicellular phenotype in premalignant breast cancers, resulting from a cross-inhibitory feedback between oncogenic receptors and nucleocytoplasmic transport regulators. In the second example, I will present an integrative framework that combines multimodal measurements, machine learning, and systems pharmacology to identify two clinically relevant metabolic phenotypes in acute myeloid leukemia. Overall, this presentation will demonstrate how an integrative, systems biology approach provides insights into the early stages of tumorigenesis and the clinically relevant heterogeneity in cancers. For more information see: https://systemsbioe.org/ *Contents* 00:00 - Introduction 08:30 - Systems Insights into Molecular Variability and Divergent Cancer Phenotypes 44:39 - Questions and Discussions Moderator: James A. Glazier, PhD, Indiana University, Bloomington If you found this video useful, please check out our other videos on computational modeling, infection and immunology: https://youtube.com/playlist?list=PLiEtieOeWbMKh9VcQoinSwODcSZKMTGat Please consider joining our IMAG/MSM WG on Multiscale Modeling and Viral Pandemics: https://www.imagwiki.nibib.nih.gov/content/msm-viral-pandemics-meetings Please also consider joining the Global Alliance for Immune Prediction and Intervention: http://glimprint.org/