Heterogeneity in multistage carcinogenesis and mixture modeling

Carcinogenesis is commonly described as a multistage process, in which stem cells are transformed into cancer cells via a series of mutations. In this article, we consider extensions of the multistage carcinogenesis model by mixture modeling. This approach allows us to describe population heterogeneity in a biologically meaningful way. We focus on finite mixture models, for which we prove identifiability. These models are applied to human lung cancer data from several birth cohorts. Maximum likelihood estimation does not perform well in this application due to the heavy censoring in our data. We thus use analytic graduation instead. Very good fits are achieved for models that combine a small high risk group with a large group that is quasi immune

Computational Cancer Biology: An Evolutionary Perspective

ISSN:1553-734XISSN:1553-735

Between-region genetic divergence reflects the mode and tempo of tumor evolution

This work was funded by awards from the NIH (R01CA182514), the Susan G. Komen Foundation (IIR13260750), and the Breast Cancer Research Foundation (BCRF-16-032) to C.C. and an award from the NIH (R01CA185016) to D.S. Z.H. is supported by an Innovative Genomics Initiative (IGI) Postdoctoral Fellowship. A.S. is supported by the Chris Rokos Fellowship. T.A.G. was supported by Cancer Research UK. This work was supported in part by NIH P30 CA124435 using the Genetics Bioinformatics Service Center within the Stanford Cancer Institute Shared Resource. The results are in part based upon data generated from the following studies: EGAD00001001394, EGAD00001000714, EGAD00001000900, EGAD00001000984, and EGAD00001001113. We thank members of the Curtis laboratory for helpful discussions