Over the past decade, complementary and at times antithetic views of tumor initiation and progression have emerged, often based on the introduction of novel high-throughput technologies for the characterization of the cell' s genetic and epigenetic landscape. On the one hand, the availability of a comprehensive map of the human genome has allowed the development of gene expression profiling techniques, mostly microarray based, to monitor the dynamic state of RNA transcripts in cancer cells. These efforts have revealed the existence of molecularly distinct subtypes of morpho-logically indistinguishable tumors, often associated with differential outcome, 1 progression, 2 and chemosensitivity. 3 They have also helped identify key genetic programs that are consistently activated (e.g., proliferation, migration, immu-noevasion), inactivated (apoptosis, senescence), or frequently modulated (adhesion, angiogenesis, etc.) in tumorigenesis. 4,5 On the other hand, genome-wide studies of both heritable and somatic human variability have moved from theoretical concept to practical reality, opening a new window on both the heritable and the somatic components of cancer etiology. Yet, even as we achieve increased sensitivity in the identifica-tion of recurrent somatic alterations for several of the major tumor types, elucidation of the mechanistic role of genetic variability in cancer remains, overall, an elusive target. Despite these advances, the relationship between genetic alterations and activation/inactivation of specific genetic programs contributing to cancer subtypes remains poorly understood, and the precise cascade of molecular events leading to tumorigenesis and progression is largely uncharted. For instance, although the mesenchymal subtype of glioblastoma is now universally accepted as a distinct sub-type, only relatively rare mutations in the NF1 gene appear to co-segregate with it, and the mechanism by which NF1 drives the subtype has not been elucidated 6 (Figure 20-1). Similarly, despite massive sequencing efforts, many muta-tions discovered in diffuse large B-cell lymphoma fail to precisely co-segregate with its two main functional sub-types, the activated B-cell (ABC) and germinal center B-cell (GCB) phenotypes, which are associated with differential outcome. 7 Even in very common tumors, such as prostate cancer, the repertoire of genomic alterations that contrib-ute to the indolent versus the more aggressive tumors is still unknown. 8 Critically, because of impractical requirements for cohort sizes 9 and lack of methodologies that maximize power for such detection, few epistatic interactions and low-penetrance variants have been identified so far. 10 This chapter introduces a set of novel approaches and strategies, mostly developed over the past decade, for the elucidation of mechanisms associated with cancer ini-tiation, progression, and chemosensitivity that, overall, go under the name of cancer systems biology. A fundamental departure from the previous methodologies is that, instead of being driven by the isolated analysis of a specific data modality, such as genomic alterations or gene expression pro-files, the new discipline is both highly integrative and, more importantly, model driven. By the latter term, we mean that cancer-related datasets are analyzed using small-or large-scale models of the cellular machinery that is most likely to have generated it. These models are still in their infancy and are largely imperfect and incomplete. Yet, even in this embry-onic state, they are starting to provide significant new insight and dissecting power, which is only going to increase as the models become more accurate and comprehensive.
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