Personalized Genomics: The First Report of a Cancer Genome

Image: A Strand of DNA

As reported by the New York Times last week, Washington University recently completed a study that decoded the entire genome of a cancer patient. The patient, a woman in her 50s who died of leukemia, became the first cancer patient and the first female to have her entire genome decoded.

To protect the patient’s identity, the data will be available to scientists only.

While the finding won’t help patients right away, the controversial project, funded primarily by a private donor, could open the door to entirely new approaches to treat cancer.

A cancer expert not involved with the study, Dr. Stephen Nimer, chief of the hematology service at Memorial Sloan-Kettering Cancer Center, called the research a “tour de force” and the report “a wonderful paper.” He said the whole-genome approach seemed likely to yield important information about other types of cancer as well as leukemia.

While scientists have debated the validity of sequencing the whole cancer genome, the study was possible due to recent advances in technology that have made it easier and cheaper to analyze 100 million DNA snippets.

Dr. Richard K. Wilson, director of Washington University’s Genome Sequencing Center and the senior author of the study, sees a bright future for DNA sequencing. “That’s personalized genomics, personalized medicine in a box,” he said. “It’s holy grail sort of stuff, but I think it’s not out of the realm of possibility.”

Dr. Wilson said he hoped that in 5 to 20 years, DNA sequencing for cancer patients would consist of dropping a spot of blood onto a chip that slides into a desktop computer that produces a report suggesting which drugs will work best for each patient.

Some feel that this study signals a change in the oncology drug development landscape. If personalized medicine for oncology becomes a reality, it could signal the end of the “one-size-fits-all” oncology blockbuster drug. Patient survival rates should increase, because cancer patients and oncologists will have a greater selection of drugs and more data to predict efficacy.

The real change will be in the drug development process: Biotech and pharma will need to create more varieties of drugs that affect different genomes. Efficacy rates should improve, but each drug will treat far fewer patients. Clinical trials will be smaller and faster, as many of the drugs should receive FDA Fast Track and/or Orphan Drug status.

That puts quite a burden on oncology drug developers. Not only do they need to shoulder the additional costs of creating more drugs (that will produce lower revenues per drug), but they will also have to create more efficient trial designs to handle the larger number of smaller, quicker trials for each drug.

One way to accomplish this is to run phase I and/or phase II trials in parallel instead of running multiple separate trials. For a deeper discussion of this strategy, check out Dr. Daniel Von Hoff’s 'The Complete Phase Ib' Insider Abstract.

But the biotech and pharma industries will adapt. If the personalized genomic predictions are correct, the burden will be worth it when powerful treatments are developed to match common genomes.