The amount of genetic variation between cancer patients is astounding. Two people diagnosed with cancer in the same organ may, in fact, have two very different diseases. Consequently, we are now in the middle of a transition: we no longer classify cancer based solely on its tissue of origin, but also on the key mutations driving growth and spread of a patient’s tumor.
This concept of diagnosing and eventually treating based upon the mutations in the tumor is well known within the cancer research community, and is termed personalized or precision oncology. Precision oncology—and, in broader terms, personalized medicine—has received tremendous attention recently. President Obama proposed a$215 million investment for the National Institutes of Health focused on precision medicine, of which $70 million would be allocated to the National Cancer Institute. This type of effort hopefully signifies a new era of research funding that should lead to advances, not just in the understanding and treatment of cancer, but of other diseases as well.
Despite the influx of funding and increased research effort around the world, precision oncology still faces significant challenges. First and foremost, we must understand the biology behind the driving mutations. I have heard arguments along the lines of “if it works, who cares?” In my opinion, this is not the right approach; without understanding the biology and making intellectually informed decisions based on the science of the disease, we will never effectively match the treatment to the disease, which, in essence, is what precision oncology is all about. Strictly employing genomic technologies will not lead to a complete understanding of the biology, and we should therefore not forgetour roots in traditional molecular biology, cell biology, and biochemistry.
Another, related challenge is to use this biology in conjunction with the growing genomic data to develop treatments that target driver mutations. While we know that not every mutation is actionable, we do have excellent success stories such as Gleevec in chronic myelogenous leukemia, ALK inhibitors in lung cancer, and mutant B-RAF inhibitors in melanoma. But mutations still evade us. Ras mutations, for example, are widely observed across multiple tumor types, but a successful Ras inhibitor has not yet made it to the clinic, despite some promising recent data toward targeting and inhibiting the protein (Nature, 503:548-51, 2013). Clearly, we are still dealing with a large gap between discoveries at the lab bench and treatments at the bedside.
A third challenge may in fact be the most ominous: tumor heterogeneity within a single patient. One recent study of lung cancer patients, for example, showed that different masses within a patient could be classified as genetically different tumor types (Science, 346:251-56, 2014). This directly impacts treatment, as it indicates that a therapy designed for a driver mutation found in one area would be unlikely to impact those in other regions. The authors state, “The regionally separated driver mutations, coupled with the relentless and heterogeneous nature of the genome instability processes, are likely to confound treatment success in NSCLC [non–small cell lung cancer].”
Nevertheless, genomic approaches are revealing a tremendous amount of information and leading to a new conceptual framework for our understanding of cancer signaling, diagnosis, and treatment. Such methods are also directly impacting patient care, with precision oncology tumor boards composed of genomicists, ethicists, bench scientists, oncologists, and surgeons meeting to discuss the best course of action for a patient given his or her tumor genotype. While these concepts are still in their infancy, they represent a new paradigm for cancer treatment. Ultimately, it is our collective understanding of the science and our ability to communicate this knowledge to our peers and patients that will support the success of truly personalized medicine.