Translational sciences: Shouldn’t we go beyond genomics?
Cancer is one of the most characteristic examples of a disease for which the scientific community has come together to systematically produce vast amounts of genomic data. The aim is to gain a better understanding of this disease in order to create targeted therapeutic compounds and companion diagnostics, and improve cancer care. These large, ambitious projects are not unjustified – cancer is not only one of the most common diseases afflicting the modern world, but also one of the most complex.
At the genomic level, our understanding of cancer points to the existence, or gradual accumulation, of genomic alterations that are responsible for the disease manifestation; these alterations are commonly referred to as "drivers."1,2 There are, however, innocent bystander genomic alterations that do not have an oncogenic potential; these are commonly referred to as "passengers." Therefore, any attempts to understand how cancer appears, evolves, and spreads may need to start with the fundamentals of separating the "passengers" from the "drivers" and then attempting to create detailed catalogs of genomic alterations in different cancer types. This is the goal of large-scale international collaborative efforts such as The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC). More recently, approaches to the isolation and characterization of circulating tumor cells3 may provide an additional view to understanding the molecular drivers of cancers.
TCGA is now in the middle of an ambitious five-year program to chart the genomic changes of 20 different types of cancer – brain, breast, gastrointestinal, gynecologic, head and neck, hematologic, skin, thoracic and urologic – by using a variety of high-throughput methods. At the same time, ICGC is attempting to go one step further by aiming to obtain comprehensive catalogs not only of genomic, but also transcriptomic and epigenomic changes in 50 different tumor types.
Those ambitious projects as well as numerous others have generated vast amounts of data that are beginning to unravel the complexity behind cancer. Recent findings suggest the existence of intratumor heterogeneity which can lead to underestimation of the complexity of the tumor genomics landscape.4 Molecular subclassifications of tumors are being characterized and found to have different combinations of driver mutations.5
So how does understanding the molecular etiology of cancer translate to personalized medicine and improving clinical outcomes? Earlier studies have led to the generation of new therapeutics. For example, it was almost 30 years ago that the RAS gene family was implicated in cancer, but its usefulness as a biomarker in cancer management has only recently been realized. In contrast, thanks to current genomic approaches, it took approximately 10 years from the time that the BRAF V600E mutation was identified, to the market approval of a drug that targets this mutation in melanoma cancer patients.
Our current understanding of cancer, however, is far from a picture painted by the sum of genomic, transcriptomic, and epigenomic events. Though these are important, scientists know that cancers need to be studied in the context of their environment and developmental stage. Ultimately, scientists need to understand how findings at the molecular level translate in the patient and give insights on an epidemiological level – going beyond genomics to understanding the underlying cellular mechanisms.
Exciting times lie ahead for cancer researchers. While they face significant challenges in being able to interpret data from high-throughput experiments and in processing, synthesizing, and making sense of this information, once they have this at their fingertips, they will be able to drill down and find answers to the scientific questions that really matter – those that can be translated to improving clinical outcomes.
Frank Witney
1Chin L., et al. Cancer genomics: from discovery science to personalized medicine. Nature Medicine 17(3):297-303 (2011).
2Chin L., et al. Making sense of cancer genomic data. Genes & Development 25(6):534-555 (2011).
3Garnett M., et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature 483(7391):570-575 (2012).
4Gerlinger M., et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. New England Journal of Medicine 366(10):883-892 (2012).
5Curtis C., et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature Epub (2012).
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