M112 Alway Building, Medical Center
(next to the Dean's courtyard)
|DATE:||May 25, 2017|
|TIME:||1:30 - 2:50 pm|
|TITLE:||Quantifying tumor evolution through spatial computational modeling and Bayesian statistical inference|
||Christina Curtis, PhD, Msc
Assistant Professor, Departments of Medicine and Genetics, Stanford University
Co-Director, Molecular Tumor Boad Stanford Cancer Institute
Cancer results from the acquisition of somatic alterations in an evolutionary process that typically occurs over many years, much of which is occult. Understanding the evolutionary dynamics that are operative at different stages of progression in individual tumors might inform the earlier detection, diagnosis, and treatment of cancer. Although these processes cannot be directly observed, the resultant spatiotemporal patterns of genetic variation amongst tumor cells encode their evolutionary histories. For example, we recently described a ‘Big Bang’ model of human colorectal tumor growth, whereby after transformation, the neoplasm grows predominantly as a single expansion in the absence of stringent selection and where the timing of a mutation is the fundamental determinant of its frequency in the final tumor. By analyzing multi-region genomic data within a spatial agent-based tumor growth model and Bayesian statistical inference framework, we demonstrated the early origin of intra-tumor heterogeneity and delineated the dynamics of tumor growth in a patient-specific manner. The Big Bang model is compatible with effectively neutral evolution and suggests that not all tumors exhibit stringent selection after transformation, thereby challenging the de facto clonal expansion model. These findings emphasize the need for the systematic evaluation of different modes of evolution across different tumor types and methods to infer the role of natural selection in established human tumors. To address this need, we developed an extensible framework to simulate spatial tumor growth and evaluate evidence for different modes of tumor evolution. Application of this approach to multi-region sequencing data from diverse tumor types reveals different evolutionary modes and tempos with implications for how human tumors progress and ultimately how they may be more effectively treated.
Andrea Sottoriva, Haeyoun Kang, Zhicheng Ma, et al. A Big Bang model of human coloretal tumor growth. Nature Genetics 47, 209-216 (2015).
Zheng Hu, Ruping Sun, Christina Curtis. A population genetics perspective on the determinants of intra-tumor heterogeneity. Biochimica et Biophysica Acta (BBA) - Reviews on Cancer. Available online 6 March 2017.