In this talk I will discuss the major determinants of inferring fitness landscapes of cancers.  These include genotype to phenotype inference, clonal population dynamics and the impact of tumor microenvironments.  I will discuss computational methods and exemplifications on patient derived datasets pertaining to each of these concepts.  Specific methods using hierarchical Bayes' based probabilistic models to address inference of these properties of cancer will be presented (including published and unpublished work).  I will synthesize these methods into a view that treats a cancer as an evolving and dynamic system whose properties can be variously measured and modeled with advances in high dimensional technologies.   Example studies on breast cancer patient derived xenografts, ovarian cancer multi-site analysis and evolutionary dynamics of follicular lymphoma will be discussed.