In their article in this issue of ONCOLOGY, Drs. Ma and Ellis report on the clinical implications of The Cancer Genome Atlas (TCGA) project for breast cancer,[1] a collaborative effort that resulted in the integration of DNA sequencing with genome-wide profiling of the epigenome, microRNAome, transcriptome, and proteome from more than 500 primary breast cancers.[2] Potential promising applications of this technology include target discovery, refined and more accurate disease classification, and improved therapeutic direction.[3] There remain significant pitfalls, however, that hinder direct clinical interventions--including the corroboration that few tumors are" addicted" to oncogenic mutations, a high tumor heterogeneity requiring the use of combinatorial therapy, and the rapid emergence of resistance to therapy even when it is appropriately applied. In addition, there are logistical obstacles to securing effective drugs for each oncogenic alteration, and to the development of a regulatory infrastructure that would facilitate matching the right patient with the right drug or combination of drugs. An example of the challenges in clinical application of TCGA findings in breast cancer, preliminary evidence indicates that no more than 30% of patients with metastatic breast cancer may be matched to a potentially effective agent based on mutational profiling, with only a fraction of those patients deriving clinical benefit from the intervention.[4]

Despite these challenges, there remains considerable promise for additional scientific discovery that may be clinically relevant. Whole-exome sequencing performed as part of TCGA identified 30,626 somatic mutations in 510 tumors, including 28,319 point mutations, 4 dinucleotide mutations, and 2,302 insertions/deletions (indels), making it challenging to easily identify druggable mutations. To reveal potential" driver" mutations, the team identified significantly mutated genes (SMGs; those with recurrent mutations observed at a higher frequency than expected from background mutation levels across the tumors) and employed an integrated pathway approach to develop graphic models for multi-platform data analysis.[5] As Ma and Ellis point out, although only 35 genes met the criteria of SMGs, and most were relatively uncommon, identification of actionable mutations may have a major impact on treatment, owing to the prevalence of breast cancer. For example, despite the low frequency of activating human epidermal growth factor receptor type 2 (HER2) mutations in HER2-nonamplified/nonoverexpressed breast cancers (2%), the mutation pattern and functional studies indicate they are likely driver mutations that may be particularly sensitive to neratinib, an irreversible pan-HER inhibitor.[6] A major challenge, therefore, is to leverage all of the molecular information that is currently hidden from view.