Scientists at the National Cancer Institute (NCI), have created a model that predicts the survival of follicular lymphoma patients based on the molecular characteristics of their tumors at diagnosis.
The model is based on two sets of genes--called survival-associated signatures--whose activity was found to be associated with good or poor prognosis for patients with the cancer. The scientists' results, to be published in the November 19, 2004, New England Journal of Medicine, suggest that immune cells infiltrating follicular lymphoma tumors have an important impact on survival--both signatures came from such immune cells.
The progression rate of follicular lymphoma, the most common non-Hodgkin lymphoma, varies widely. "In some patients the disease progresses slowly over many years, whereas in others progression is rapid, with the cancer transforming into aggressive lymphoma and leading to early death," explained principle investigator Louis M. Staudt, M.D., Ph.D., of NCI's Center for Cancer Research. "Understanding the molecular causes of such differences in survival could provide a more accurate method to determine patient risk, which could be used to guide treatment and may suggest new therapeutic approaches."
To create their model, Staudt and associates used follicular lymphoma biopsies taken from 191 untreated patients. The biopsies were taken between 1974 and 2001 and came from North American and European institutions that are part of the NCI-sponsored Lymphoma/Leukemia Molecular Profiling Project**. Following their biopsies, all patients received standard treatments. The NCI scientists examined their subsequent medical records to determine survival. Biopsies were divided into two groups balanced for survival and institution: 95 went into a group used to uncover gene expression patterns associated with survival; the other 95 were used to test the predictive power of these patterns.
NCI scientists first used a DNA micro array to determine which genes were expressed (active) in the first group of 95 tumor biopsies, and at what levels. They then determined which of these genes were statistically associated with survival. They called those associated with long survival "good prognosis genes" and those associated with short survival "poor prognosis genes."
Next, the researchers identified subsets of both kinds of genes that tended to be expressed together. These they named "survival-associated signatures." Two signatures--one which indicated poor prognosis, the other good--had strong synergy and together predicted survival better than any other model tested. Unexpectedly, both came from nonmalignant immune cells infiltrating the tumors. The good prognosis signature genes reflect a mixture of immune cells that is dominated by T cells. T cells react to specific threats to the body's health. In contrast, the poor prognosis signature genes reflect a different group of immune cells dominated by macrophages and/or dendritic cells--which react to nonspecific threats--rather than T cells.
The two signature model allowed NCI scientists to divide patients into four equal groups with disparate average survival rates of 3.9, 10.8, 11.1, and 13.6 years. For the 75 percent of patients with survival rates 10 years or longer, "watchful waiting is appropriate," Staudt said. "These patients would benefit from knowing that they may not need treatment for quite some time. On the other hand, those patients in the group with the lowest survival rate should be considered for newer treatments and clinical trials," added Staudt.
The fact 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 the immune cells with the good-prognosis signature are attacking the lymphoma and keeping it in check," Staudt speculated. "Another possibility is that 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," he added. Knowing more about the signals that may delay the spread of follicular lymphoma could provide new therapeutic targets.