NCI Cancer Bulletin: A Trusted Source for Cancer Research NewsNCI Cancer Bulletin: A Trusted Source for Cancer Research News
November 23, 2004 • Volume 1 / Number 45 E-Mail This Document  |  Download PDF  |  Bulletin Archive/Search  |  Subscribe

NCI Cancer Bulletin Archive

Page Options

  • Print This Page
  • Print This Document
  • View Entire Document
  • Email This Document
  • View/Print PDF

The information and links on this page are no longer being updated and are provided for reference purposes only.

Featured Article

Model Predicts Follicular Lymphoma Survival

National Cancer Institute (NCI) researchers have developed a model to predict survival of patients with follicular lymphoma based on the genetic "signatures" of their tumors at diagnosis. According to the model, the activity of two sets of genes - termed "survival-associated signatures" by lead researcher Dr. Louis Staudt and colleagues - was associated with either more aggressive forms of the cancer and shorter survival times, or slower moving forms of the cancer and longer survival times.

The findings, published in the Nov. 18 New England Journal of Medicine, could have implications for treatment of follicular lymphoma. Survival among follicular lymphoma patients varies dramatically, explains Dr. Staudt, a principal investigator in the NCI Center for Cancer Research Metabolism Branch. "Understanding the molecular causes of such differences in survival could provide a more accurate method to determine patient risk," Dr. Staudt says, "that could be used to guide treatment and may suggest new therapeutic approaches."

To perform gene expression profiles for this study, researchers used DNA microarray analysis, a method for quickly scanning thousands of genes for activity in a tumor sample. The researchers used the Lymphochip - a glass chip with DNA "spots" on it from approximately 18,500 genes expressed in lymph tissue - created in Dr. Staudt's laboratory to study lymphoid cancers.

Researchers analyzed follicular lymphoma biopsies of 191 patients before treatment; biopsies came from institutions participating in the NCI-sponsored Lymphoma/Leukemia Molecular Profiling Project. After biopsy, all patients received standard treatments; subsequent medical records were examined to determine survival. The Lymphochip was used to determine which genes were active in the first group of 95 tumor biopsies (the "training set") and at what levels; researchers then determined which of these genes were statistically associated with survival. Next, researchers identified subsets of good- and bad-prognosis genes that tended to be expressed together; these subsets constituted the survival- associated signatures. In the remaining 96 samples (the "test set"), two signatures - indicating poor and good prognosis - had strong synergy and together predicted survival better than any other model tested. Unexpectedly, both came from nonmalignant immune cells that infiltrate the tumors.

Based on the two-signature model, the NCI team divided patients into four equal groups with average survival rates of 3.9, 10.8, 11.1, and 13.6 years. For the 75 percent of patients with survival rates of 10 years or longer, "watchful waiting is appropriate," Dr. Staudt says. "On the other hand, those patients in the group with the lowest survival rate should be considered for newer treatments and clinical trials."

That the most predictive signatures came from immune cells suggests an important interplay between the host immune system and malignant cells in follicular lymphoma. "One possibility is that immune cells with the good-prognosis signature are attacking the lymphoma and keeping it in check," he suggests. "Alternately, these immune cells may provide signals that encourage the cancer cells not to leave the lymph node, preventing or delaying the spread of the cancer."

In 2002, Dr. Staudt's group published a study on a similar model identifying a single 17-gene signature that predicted patient survival for diffuse large B-cell lymphoma (DLBCL). This model will be used in a phase III trial testing the current standard of care for untreated DLBCL against a new regimen. Patient biopsies will undergo gene expression profiling to determine what tumor features influence patient response to the therapies.