NEW YORK (Reuters Health) - Routine genome-wide screening of cancers is likely a long way off, a new paper says.
The technology, known as next-generation sequencing, promises to revolutionize doctors' understanding of cancer and underpins perhaps the biggest paradigm shift taking place in cancer research today: the growing emphasis on a cancer's genetic makeup, rather than its location within the body.
Understanding the genetic makeup of an individual patient's tumor may allow physicians to pick the drug that best targets that specific tumor, as well as recognize when a tumor has developed resistance to the drug through new genetic mutations.
"Next-generation sequencing is especially promising in cancer because in a single test, one can interrogate all clinically relevant cancer genes for all types of genomic alterations, including sequence mutations and chromosomal rearrangements," Dr. Michael Berger, a geneticist at Memorial Sloan-Kettering Cancer Center in New York City, told Reuters Health in an email.
There are several different specific screening technologies that are considered "next-generation" - but all share the ability to sequence entire human genomes in a matter of days. When applied to cancer, the technology is used to screen the entire genome of cancer cells.
By some measures, this promise is already being realized. For example, last year The Cancer Genome Atlas Research Network used genome-wide screening of breast cancer tumors to demonstrate that there are four main breast cancer types defined by differing genomic and epigenetic mutations. The study showed that individual breast cancers have many genetic differences from each other but that one subgroup of breast cancers, basal-like breast cancer, was similar genetically to serous ovarian cancer.
Cancer cells present unique and complex challenges, they note. Because they are genetically so different from normal human tissue, there is not always a 'reference sequence' against which to compare the tumor DNA. There are also frequent chromosome-scale as well as epigenetic changes, and even significant genetic differences among cells within the same tumor, an issue specific to cancer cells known as tumor heterogeneity.
The authors of the new paper, writing online July 25 in the British Journal of Cancer, pointed out that this complexity creates a number of problems that must be solved before next-generation sequencing is a common part of cancer care.
One of the first issues is developing the algorithms that are used to map the genome.
"The computational challenges involved in analyzing and storing clinical (next-generation sequencing) data cannot be overstated," said Dr. Berger, who wasn't involved in the new study. "Better algorithms must be developed to reliably and accurately detect mutations in heterogeneous tumors."
In genome-wide sequencing, a seemingly minuscule misstep in the analysis could have massive consequences. For example, say the authors of the new paper, led by Dr. Danny Ulahannan of the Wellcome Trust Center for Human Genetics in Oxford, UK, "the sheer quantity of data means that getting 0.01% of the human genome wrong would correspond to 300,000 errors scattered along the three billion base pairs."
Dr. Lynda Chin, the chair of Genomic Medicine and scientific director of the Institute for Applied Cancer Science at MD Anderson Cancer Center, told Reuters Health this is often an overlooked problem.
"One barrier that is often overlooked or underestimated from the clinical side is the technical challenge of generating high-quality (next-generation sequencing) data," Dr. Chin said. "There is a sense that generating (the data) is easy, and it is the analysis that is hard. I would disagree, as I believe that the technology is still unstable, for lack of a better word, not yet turn-key, and no matter how good the analytics-interpretation become, if the data is poor quality, the result will be poor."
And mapping the genome is really only the first step. The next step is figuring out which mutations are relevant to the development of cancer and whether they can be targeted with a drug.
"I agree with the obvious barriers of interpretation. Not just analytically that we need improved algorithms, (but) more importantly, more knowledge and understanding of what each alteration means and how each event impact on clinical decision," Dr. Chin said.
The advances will require a "cultural change" in cancer research, Dr. Chin said, that makes "patient-oriented genomic research a standard, rather than a heroic effort by a researcher."
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