A recent advance in personalized cancer medicine is the use of
mouse avatars, or patient-derived xenografts, to help identify
which drug or drug combinations are most likely to be effective
for an individual cancer patient. While this remains far from being
widely used, its promise has been clearly demonstrated (171).
Patient-derived xenografts are generated by implanting portions
of a patient’s tumor into several mice. A large number of potential
therapies can then be tested on the mice for their ability to
destroy the patient’s tumor before they are given to the patient.
This pretreatment screening increases the likelihood that a given
treatment plan will benefit the patient, and eliminates exposure to
therapies from which the patient is unlikely to benefit.
In one of the first clinical studies of treatment guided by patient-derived xenografts, avatars were created for 14 patients with
advanced cancers nonresponsive to current standard-of-care
therapies. In this study, avatar screening successfully identified
an effective treatment strategy for 12 of the patients. Importantly,
these treatment strategies would not have been offered to these
patients without the avatar screening. Moreover, as a result of the
drugs identified through the avatar screening process, one of the
patients who had advanced pancreatic cancer was disease free
more than six years after diagnosis (172).
In addition to their clinical potential, patient-derived xenografts
are also being used in the research setting to enhance our
understanding of cancer biology and to accelerate drug
development. For example, some researchers are starting to use
patient-derived xenografts to help them select which drugs should
be evaluated in clinical trials and for which patient groups.
Despite their promise, there are many challenges to using
patient-derived xenografts. First and foremost, they are difficult
to generate, and the success rate for implanting human tumors
in mice is low. Second, it takes more than six months to generate
patient-derived xenografts and screen potential therapies. Most
patients do not have that much time. Last, to treat one patient,
many avatars have to be generated, which costs tens of thousands
of dollars. However, if these obstacles are overcome, patient-derived xenografts may help many more patients in the future.
This approach, however, requires that we understand enough
about the underlying biology of cancer to be able to accurately
predict the alternative growth advantages most likely to be
used by the cancer. The knowledge required to do so will come
from a variety of sources including whole-genome sequencing,
patient-derived xenograft testing (see sidebar on
Patient-derived Xenografts), and predictive mathematic modeling of
cancer behavior using systems biology and evolutionary theory.
Even armed with this deeper understanding of cancer biology,
much work needs to be done to determine the order, duration,
and dosing of the combination of anticancer agents being
used. Here again, mathematical modeling and systems biology
approaches will be critical to narrowing the nearly infinite
permutations into a manageable subset that can be tested
in clinical trials. Perhaps one of the most intriguing areas of
combination therapy will be adding the new immunotherapies
to radiotherapies, chemotherapies, and molecularly targeted
therapies to enhance clearing of the tumor by the immune
system (see Special Feature on Immunotherapy, p. 38).
We are just beginning to mine our cache of existing tools
and drugs to develop rational combinations that are likely to
provide better and more durable cancer responses than any
of the agents alone. Continued research will speed these
breakthroughs within the next few years.
The Distant Horizon
Of the nearly 3 billion bases in the human genome, only
about 1. 5 percent code for the various proteins a cell uses to
function. Just more than a decade ago, researchers made the
important discovery that some of the remainder of the genome
codes for molecules called noncoding RNAs, which naturally
regulate gene usage and therefore the production of proteins.
Since that discovery, small, synthetic noncoding RNAs
and DNAs have become vital tools in research laboratories
worldwide. Researchers are also exploring the possibility that
their ability to dampen gene usage and protein production
can be exploited for patient benefit. Unfortunately, the use of
synthetic noncoding nucleotides (DNA or RNA) in this way has
been hampered by our inability to effectively deliver them to a
When combined with whole-genome sequencing, the clinical
use of small, synthetic noncoding nucleotides could potentially
revolutionize cancer treatment. Here, the identification of
the exact mutations fueling an individual’s cancer would be
identified through whole-genome sequencing. An anticancer
therapeutic composed of small, synthetic noncoding
nucleotides would then be prepared to potentially eliminate the
abnormal protein(s) produced by these mutations, negating the
competitive growth advantage of the cancer cells.
Because of the potential power of small, synthetic noncoding
nucleotides, this is, and has been, a very active area of
research. The infrastructure and technology to produce such
therapeutics are already in place, and in some cases in early
clinical testing. Given our rapid pace of discovery across all
scientific sectors, it is likely that anticancer therapies based
on small, synthetic noncoding nucleotides will one day benefit
patients. Continued progress is incumbent upon continued
research and investment in this area.