Comprehensive analysis of cancer genomes has revealed a great deal of diversity in the genetic abnormalities found within cancers of a single type. Moreover, recurrent genetic alterations within these cancers are often involved in only a small percentage of cases. Discovering rare genetic alterations that drive cancers is therefore a challenge for the field.
Another challenge is acquiring high-quality biological samples needed for genomic studies, particularly for tumor types that are uncommon or rare, or those not treated primarily by surgery.
Developing cell lines and animal models that capture the diversity of human cancer is also an unmet need. Models of rare cancer subtypes may be nonexistent or underrepresented, and there are no models for many recurrent genetic lesions in human cancer.
Managing and analyzing the vast amounts of data involved in genomic studies are additional challenges for the field. This area of research requires an efficient bioinformatics infrastructure and increasingly involves contributions of data and expertise from cross-disciplinary teams.
