Getting Personal with Oncology

Many cancer drugs are effective in only a small percentage of patients. This raises a somewhat profound question: how do we know that a certain drug will work well for some patients? If a potential drug cures 3 percent of patients, the data may never be found in the population at large. Likewise, adverse effects on a different – but still small – percentage of the total population, may be lost.

One proposed solution is personalized medicine. Precision (or personalized) medicine has great potential, but most efforts focus on finding a genomic answer. Whether it is the National Institutes of Health website (1), or an interview with Joe Biden about the Cancer Moonshot (2), the technology focus du jour seems to be genomic analysis. Genomics has revealed much about cancer; it has led to new targets for therapy and offers much promise for the future, but it’s important to look beyond the hype. The outcome of genomics-based testing is often less effective than proponents might imply.

I am far from Olympian, but I will beat you over a one-mile track if you hop the entire way… (3) And right now, we are hopping when it comes to personalized medicine if we rely completely on genomics. To improve our outcomes, we need to use other tools alongside genomics. I believe there are three significant problems associated with the genomics approach to cancer treatment.

Problem one: DNA mutations do not always cause cancer

Having a mutation, even a putative cancer-causing mutation, is not evidence of cancer, or of the cause of a particular cancer. Researchers at the Wellcome Trust Sanger Institute (WTSI) observed that healthy skin tissue is replete with somatic DNA mutations. In fact, those researchers found mutations in healthy skin at the same level as in tumor cells (4). Not only did a quarter of all healthy skin cells carry a cancer-causing mutation, but these mutations were under strong positive selection – that is, the number of cells carrying the mutations tended to increase.

In this case, if an oncologist found a “cancer-causing mutation” in a cancer patient, prescribing chemotherapy based on the finding would have no impact on the tumor. Unfortunately, the chemotherapy could still carry negative side effects for the patient, including mouth sores, hair loss, diarrhea, nausea, vomiting, and fatigue. Throughout this, the cancer, unaffected by the chosen course of therapy, would continue to grow and mutate.

Researchers at Johns Hopkins showed that DNA analysis of tumor cells tended to produce many false positives (5). They suggest that comparing mutations in healthy tissue with those in the tumor will determine the true, cancer-causing genes. Unfortunately, data from the WTSI shows that healthy tissue contains many different mutations. It is unlikely that there is a single healthy genotype to use for comparison.

Problem two: Lab results vary

Quackenbush and colleagues compared genotypic and phenotypic results published by the Cancer Genome Project and the Cancer Cell Line Encyclopedia. They found that the two labs generated almost identical genomic results for 471 different cell lines (5). Yet, the phenotypic response of those cell lines diverged between the two labs. Where one lab found a particular drug to be effective in a cell line, the second lab gave contradictory results. Of the many drug/cell line combinations, only two of the 15 drugs tested in both labs showed any correlation. Chemotherapy based on the response of cell lines is problematic, at best.

Problem three: Personalized medicine is percentage driven

Most personalized medicine is not personalized at all – and is instead based on the responses of small patient groups. Therapy is based on how a percentage of patients with the same mutation and similar cancer responded. If the doctor determines the cancer-causing mutation among the somatic mutations, he or she may find a mutation for which there is no effective chemotherapy. The pharmacogenomic process can point you only to treatments that have already been verified in a population. While the number of therapies grows, a genomics-based approach would miss cancers curable by drugs not associated with a particular mutation. If previous personalized medicine efforts have not linked a drug-mutation combination with a successful outcome, the doctor’s options are limited.

From hopping to running

How can we enhance the existing paradigm of personalized medicine to provide better cures? How do we move from hopping to running? The potential of genomics is great but we need orthogonal techniques as well. Researchers at the Institute for Molecular Medicine, Finland (FIMM) are developing an individualized approach to cancer treatment (6)(7)(8)(9)(10)(11)(12)(13). At FIMM, they isolate cancerous cells from a patient and then test those cells against hundreds of possible drugs to see which are effective. They test drugs singly, and in combination, to identify those most potent against the patient’s particular cancer cells. Even if a drug is only effective in one percent (or fewer) cases, it can still be discovered and prescribed.

Using this approach, FIMM researchers have identified treatments for patients who had already failed multiple rounds of traditional chemotherapy. They have also been able to save patients from ineffective treatments. In some instances, a drug may be effective against isolated cancer cells ex vivo, but may not work inside the body. But it is also true that if a drug does not work ex vivo, then it is unlikely to work in the patient. So, when DNA mutations suggest a particular drug, but ex vivo tests show that it will be ineffective in a particular patient, the FIMM protocol eliminates a futile round of chemotherapy. No loss for the insurance company, no chemo-induced side effects for the patient, and no wasted time during which malignancy can further develop.

FIMM’s functional testing is not done in isolation. The researchers couple the results of the drug sensitivity tests with genomic results to gain information about the origin of the cancer and to help define new therapies to address new-found mutations. Using this technique, they showed that the anti-angiogenic renal cancer drug, axitinib, is very effective against a form of chronic myeloid leukemia (14). Such unanticipated repurposing of an existing drug is beneficial to both the patient and the drug manufacturer. These results suggest that other drugs that have passed Phase I – but failed to show efficacy in large-scale studies – could be repurposed.

Labs in Sweden and Spain are now expanding the efforts initiated by FIMM (15)(16)(17)(18), but more labs must join the effort. Researchers in Spain have reported an ex vivo approach that keeps the tumor cells in their native microenvironment, significantly enhancing the quality of their results (19).

Chemotherapy has both monetary and health costs whether it leads to a cure or not. Every failed round of treatment reduces the window of opportunity to cure the disease. New methods, like those from FIMM, can save lives, reduce relapses, lessen costs, and help discover new drugs. So why don’t we see researchers testing the FIMM protocols? And why don’t we see extensive ex vivo testing occurring? When I mention the FIMM process to researchers, many have a few objections. Most importantly, they do not believe that they can get funding to do follow-up research since the technique failed to be effective in the 1970s and 1980s. Instrumentation, assays and cell manipulation techniques, however, are much more powerful today than they were 30 or 40 years ago. A greater impediment may be that researchers (and reviewers) think of personalized medicine as a genomics-based technique and do not consider the importance of orthogonal methods. The combination of the ex vivo and genomics-based techniques brings a second leg to the race and running faster along this track is a boon to everyone: doctors, pharma companies, insurers, and – most importantly – patients.

Joe Olechno is Senior Research Fellow, Labcyte Inc., San Jose CA, USA.