Advancing personalized medicine for cancer to the clinic is a relatively new area and full of challenges because almost every cancer presents a unique set of molecular changes.1 Combine that with the fact that each patient will express proteins at different rates given different conditions, and the complexity of proteomics in cancer becomes staggering. Nevertheless the goal to offer therapies targeted to particular cancers optimized for specific patients is so compelling that researchers and drug companies are working hard on many fronts.
The study of protein characterization and expression in cancerous cells has expanded rapidly as methods, such as Genome Wide Association Studies (GWAS), and new technologies, including fluorescence in situ hybridization (FISH), microarrays (MA), and protein expression by immunohistochemistry (IHC), have been developed.2 These techniques allow investigators to develop molecular profiles that characterize the underlying molecular structure of tumor cells. From there, they then develop highly targeted therapies that promise to offer more effective therapies with less severe side effects than generic cytotoxic agents typically used in cancer treatment. The following examples have succesfully moved from the research bench to clinical utlility in cancer treatment.
Cancer Treatment: HER2
One of the first applications to reach the clinic is testing for Human Epidermal Growth Factor Receptor 2 (HER2), a protein encoded by the ERBB2 gene. Amplification of ERBB2 occurs in about 30 percent of breast cancers3 and is strongly associated with increased recurrence and poorer prognosis.
HER2 has become both an important biomarker and target for treatment. Clinicians now test breast cancer patients to assess prognosis and determine suitability for therapy with the monoclonal antibody trastuzumab, which targets HER2. Marketed as Herceptin, trastuzumab is effective only in cancers where HER2 is overexpressed. When trastuzumab binds to HER2, there is a resultant increase in p27, a protein that halts cell proliferation.4 It is also important that trastuzumab is restricted to HER2-positive individuals due to its expense and association with cardiac toxicity.
Adverse Reaction: UGT1A1
In addition to identifying novel compounds for therapeutics, researchers have found protein-based biomarkers predictive of adverse reaction in drug therapy. Irinotecan, a drug used in the treatment of colorectal cancer, inhibits topoisomerase I, preventing re-ligation of single-stranded DNA breaks created during cell replication.5 Because the DNA damage cannot be effectively repaired, the tumor cell dies. Unfortunately for some patients with a particular uridine diphosphate gluconosyltransferase 1A1 (UGT1A1) allele, irinotecan causes neutropenia. The U.S. FDA now recommends lower dose irinotecan for patients testing homozygous for the UGT1A1 *28 allele.6
While new developments in methods and technologies have promoted a flurry of research activity in personalized medicine and pharmacogenetics for cancer, there are relatively few therapies or diagnostics approved for use in clinics today. These early examples of the use of protein characterization for personalized medicine in cancer are just the beginning of massive new investment in research in proteomics.
1. Stricker, T., Catenacci, D.V.T., Siewert, T.Y. (2011) ‘Molecular profiling of cancer–the future of personalized cancer medicine: A primer on cancer biology and the tools necessary to bring molecular testing to the clinic‘, Seminal Oncology, 38 (2), (pp. 173-185)
2. Raheela, A. (2012) ‘Molecular profiling for personalized cancer care‘ Clinical and Experimental Metastasis, published online June 8, 2012. doi: 10.1007/s10585-012-9483-3
3. Tan, M., Yu, D. (2007) ‘Molecular mechanisms of erbB2-mediated breast cancer chemoresistance‘, Advances in Experimental Medicine and Biology, 608, (pp. 119-129)
4. Le, X.F., Pruefer, F., Bast, R. (2005) ‘HER2-targeting antibodies modulate the cyclin-dependent kinase inhibitor p27Kip1 via multiple signaling pathways‘, Cell Cycle, 4 (1), (pp. 87-89)
6. Daly, A. (2010) ‘Genome wide association studies in pharmacogenics‘, Nature Reviews Genetics, 11 (4), (pp. 241-246)