What are we learning from the cancer genome?

Abstract

Massively parallel approaches to nucleic acid sequencing have matured from proof-of-concept to commercial products during the past 5 years. These technologies are now widely accessible, increasingly affordable, and have already exerted a transformative influence on the study of human cancer. Here, we review new features of cancer genomes that are being revealed by large-scale applications of these technologies. We focus on those insights most likely to affect future clinical practice. Foremost among these lessons, we summarize the formidable genetic heterogeneity within given cancer types that is appreciable with higher resolution profiling and larger sample sets. We discuss the inherent challenges of defining driving genomic events in a given cancer genome amidst thousands of other somatic events. Finally, we explore the organizational, regulatory and societal challenges impeding precision cancer medicine based on genomic profiling from assuming its place as standard-of-care.

Figure 1

Heterogeniety in Cancer. A breast cancer patient has a primary breast tumor, consisting of three sections (P1, P2, P3), and metastases in the brain (MB), liver (ML) and hip bone (MO). While all the tumors from all sites are related, and differ from the germline genome, their degree of relatedness can be interrogated with genome evaluation. Conceptual patterns of relatedness emerging are those malignancies that continue to evolve as they metastasize or in response to treatment (the Oak tree model), or those that are more uniform(the Palm tree model).

Figure 2

Gain of function mutations in Notch genes are recurrent in T-ALL whereas loss of function mutations predominate in squamous cell cancers of the skin, lung and head and neck.

Figure 3

PARADIGM approach to pathway analysis of a drug sensitive vs. drug resistant population of patients.

Figure 4

An example of an integrative approach to pathway analysis of a drug-sensitive versus drug-resistant population of patients.