Scientists have discovered that the most dangerous cancer of the uterine lining closely resembles the worst ovarian and breast cancers, providing the most telling evidence yet that cancer will increasingly be seen as a disease defined primarily by its genetic fingerprint rather than just by the organ where it originated.
The study of endometrial cancer the cancer of the uterine lining and another of acute myeloid leukemia, published simultaneously on Wednesday by Nature and the New England Journal of Medicine, are part of a sprawling, ambitious project by the National Institutes of Health to scrutinize DNA aberrations in common cancers.
Over the past year, as part of this project, researchers have reported striking genetic changes in breast, colon and lung cancers that link them to other cancers. One kind of breast cancer was closely related to ovarian cancer. Colon cancers often had a genetic change found in breast cancer. And about half of squamous cell lung cancers might be attacked by drugs being developed for other cancers.
The endometrial cancer and leukemia efforts alone involved more than 100 researchers who studied close to 400 endometrial tumors and 200 leukemias. Endometrial cancer is the most common gynecological cancer in American women and strikes nearly 50,000 of them a year, killing about 8,000. Acute myeloid leukemia, the most prevalent acute adult leukemia, is diagnosed in about 14,000 Americans a year and kills about 10,000.
"This is exploring the landscape of cancer genomics," said Dr. David P. Steensma, a leukemia researcher at the Dana-Farber Cancer Institute who was not involved with the studies. "Many developments in medicine are about treatments or tests that are only useful for a certain period of time until something better comes by. But this is something that will be useful 200 years from now. This is a landmark that will stand the test of time."
Dr. Douglas Levine of Memorial Sloan-Kettering Cancer Center, the principal investigator on the endometrial cancer study, said the group scoured the country for samples of this cancer.
The cancer has long been evaluated by pathologists who examine thin slices of endometrial tumors under a microscope and put them in one of two broad categories. But the method is not ideal.
In general, one category predicts a good prognosis and tumors that could be treated with surgery and radiation, while the other holds a poorer prognosis and requires chemotherapy after surgery. But pathologists often disagree about how to classify the tumors and can find it difficult to distinguish between the two types, Levine said.
The new genetic analysis of hundreds of tumors found patterns of genetic aberrations that more precisely classify the tumors, dividing them into four distinct groups. About 10 percent of tumors that had seemed easily treated with the old type of exam now appear to be more deadly according to the genetic analysis and would require chemotherapy.
Another finding was that many endometrial cancers had a mutation in a gene that had been seen before only in colon cancers. The mutation disables a system for repairing DNA damage, resulting in 100 times more mutations than typically occur in cancer cells.
"That was a complete surprise," Levine said.
It turned out to be good news. Endometrial cancers with the mutation had better outcomes, perhaps because the accumulating DNA damage is devastating to cancer cells.
Another surprise was that the worst endometrial tumors were so similar to the most lethal ovarian and breast cancers, raising the tantalizing possibility that the three deadly cancers might respond to the same drugs.
Jeff Boyd, executive director of the Cancer Genome Institute at Fox Chase Cancer Center, who was not involved with the new research, said the similarity among breast, ovarian and endometrial tumors was the best example yet of the idea that cancers are more usefully classified by their gene mutations than by where they originate.
Though many scientists believe this view is correct, Boyd said, "It is very rewarding I can't overstate it" to see it validated with real data.