The common assumption about metastasis is that a primary tumor starts off benign and over time acquires mutations that provide a few rare cells within the tumor the ability to metastasize. This process, known as multistep tumorigenesis, is the basis for stressing the value of early detection because it is believed that if a tumor is detected and treated before it spreads, then the chances of survival are increased.
However, two new studies used microarrays to challenge that assumption and suggest that acquisition of the metastatic phenotype may not necessarily occur late in development for all tumors. Some tumors may actually start out deadly to begin with.
The first study, led by René Bernards, Ph.D., professor of molecular carcinogenesis at The Netherlands Cancer Institute, Amsterdam, included an analysis of tissue specimens from 295 women treated for stage I or II breast cancer who were younger than age 53. Disease outcome information was available through a patient medical registry.
Microarray analysis identified a genetic fingerprint that was predictive of whether the cancer was going to be deadly regardless of early detection and treatment. The study, which was published in December in the New England Journal of Medicine, found that those patients presenting with a good prognostic fingerprint had a 95% chance of surviving the next decade, whereas those with a bad fingerprint had only a 55% chance of surviving.
If all of these women were diagnosed and treated at early stages of disease, why was there such a large difference in outcome?
"We think its a combination of initiating events in breast cancer. Some [tumors] are pre-ordained to spread and some will have a favorable combination of initiating events making them less likely to spread," said Bernards.
This may explain why the magnitude of benefit of early detection has proven disappointing. Mammograms can detect very small tumors, yet the breast cancer death rate is not dramatically different between women who have mammograms from women who do not. By the time a small tumor is removed, microscopic metastases may, in many cases, have already taken root throughout the body.
"The window of opportunity for successful treatment may only exist in a small number of women; however, this may not be the prevailing model of metastasis," said Bernards.
Results from a separate study, led by Todd Golub, M.D., and published in December in Nature Genetics, supports this hypothesis and suggests that it may hold true for all solid tumors.
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"There are many unanswered questions that need to be addressed before a real understanding of metastasis is obtained," said Golub. "There are aspects of tumor behavior that are not governed by their inciting genetics at all, and so I suspect that such predictors will never be perfect." The observations made in both studies are being extended to larger numbers of patient samples.
This hypothesis of metastasis differs from the multistep tumorigenesis theory in that the ability to metastasize is an inherent quality of the tumor from the beginning. In addition, it is not just a few rare cells in the tumor that acquire metastatic ability, but all cells within such tumors have the ability to metastasize.
Clinical Significance
Current prognostic criteria for breast cancer, including age and lymph node status, have proven to be poor markers. For example, about 30% of patients who do not present with lymph node involvement will eventually develop metastases anyway. Who are these 30%? And should everyone be treated aggressively so these patients are not missed? Right now, most patients are treated as if their tumor will spread, meaning that 70% of patients without lymph node involvement are overtreated.
"Patients are asking, What are my chances? With current predictors we cant give them a number. But with microarrays, we can give these numbers," said Bernards. The genetic fingerprint associated with metastasis may help to identify those patients who need aggressive treatment from those who do not, which will help to address overtreatment/undertreatment issues that can plague oncologists.
"Microarrays are one of the first of what will be a series of new technologies that provide a closer look at the workings of cells at a molecular level," said Richard Simon, D.Sc., chief of the Biometric Research Branch at the National Cancer Institute.
According to Simon, these are considered the newest types of microscopes because microarrays allow scientists to "see" levels of gene expression for thousands of genes at one time, on one tiny chip. Focusing on gene expression patterns of a multitude of genes, referred to as genetic fingerprints, is proving to be a much more powerful approach than studying a small number of genes that may not hold much clinical significance individually.
"There is tremendous power in having a multi-analyte (multi-gene) diagnostic test because the measurement precision of any given gene then becomes less important. If the test is based on a single gene (or a very limited number of genes), measurement accuracy becomes crucial. The genome has a built-in robustness and redundancy that we should take advantage of, and this can be done most easily using a parallel platform such as a microarray," Golub said.
In addition to serving as a prognostic tool in the clinic, those genes that prove to be associated with poor prognosis by microarray analysis can now become the next generation of molecular targets.
And how soon will we see the use of microarray technology in the clinic? Bernards does not think that microarrays will replace classical prognostic criteria right away, but within a few years they will be used in addition to other predictors.
"I think well first see such approaches entering the clinical trial process, guiding patient selection based on genetic factors. Routine clinical implementation will follow, but its hard to know how long this will take," said Golub. "The greatest challenge will not be the technology per se, but rather the execution of sufficiently large validation studies to ensure that such tests are really worthy of clinical implementation."
Word of Caution
From a statistics perspective, microarray studies will need to control for multiple comparisons because microarrays provide levels of expression for thousands of genes, Simon said. For example, "In comparing two classes of tissue with regard to 10,000 genes, the expected number of false positive genes that appear differentially expressed between the two types at the 5% significance level is 500," he said. This highlights the importance of validation in every microarray study design.
Simon said he feels that most biologists do not understand that theory and mathematical modeling must play a greater role than it currently does for biology to fully utilize microarray technology and allow it to mature as a science.
"In order to move forward, biologists must accept biomathematicians and systems biologists as equals in the relevance of their research approach for elucidating biological principles, and for understanding and reducing the burden of human disease," he said. "Part of this process is the need for greater equal collaboration between biomedical scientists and biomathematicians and biostatisticians in the design and analysis of microarray studies."
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