Workshop in Biostatistics

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
SPEAKER:
Christina Curtis, PhD, Msc
Assistant Professor, Departments of Medicine and Genetics, Stanford University
Co-Director, Molecular Tumor Boad Stanford Cancer Institute

 

Abstract:

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.

Suggested readings:

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.