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Scientists
Differentiate Cancers
WASHINGTON
(AP) - By combining gene technology and high-speed computers that
learn as they go, scientists have determined a way to tell the
difference among several childhood cancers that appear similar.
Being able
to differentiate these diseases quickly allows doctors to improve
treatment.
In the long
run, they hope, it will lead to better ways to battle the diseases.
``Basically,
we are merging two technologies,'' explained Dr. Paul Meltzer
of the National Human Genome Research Institute, a part of the
National Institutes of Health.
The study,
reported in the June issue of the journal Nature Medicine, focused
on small, round blue-cell tumors of childhood, a group of four
types of cancer that appear similar but respond to different treatments.
Those tumors include neuroblastoma, rhabdomyosarcoma, non-Hodgkin's
lymphoma and the Ewing family of tumors.
``These cancers
are difficult to distinguish, ... and currently no single test
can precisely distinguish these cancers,'' the scientists reported.
The first
step involves microarrays, which are about the size of a microscope
slide and contain specific fragments of specially chosen DNA,
the genetic building block.
An active
gene sends out a chemical signal called RNA, which carries a particular
sequence of building blocks that tell what gene it came from.
A tissue sample
from the cancer is spread on the microarray, and each RNA string
then attaches to a waiting DNA segment designed to attract it.
By detecting
how many strings end up at each site on the chip, instruments
can tell how active the corresponding gene had been in the tumor.
Many strings signify a very active gene. No strings at a particular
site means the corresponding gene had been turned off.
This information
is then fed into a computer program called an artificial neural
network, which has learned which gene segments are most active
for differing types of cancer.
``Essentially
it's a computer program that does an artificial learning process,''
Meltzer explained. ``The data goes through a training phase, and
the computer tries to learn the features of the data that allow
it to make a classification.''
Once the neural
network had learned to classify the types of cancer, it was tested
in 88 different experiments and ``essentially got them all categorized
correctly,'' Meltzer said.
``This is
something which, in one form or another, is likely to actually
be tested in a clinical setting in the near future,'' he said.
He wouldn't speculate how soon.
In addition,
Meltzer noted, the process allows scientists to learn gene profiles
of the various types of cancer.
``When you
discover these profiles, or fingerprints or signatures that represent
the profile of gene expression for a given cancer, you're also
getting lists of genes ... for possible new therapeutic targets,''
he said.
Yudong He
and Stephen Friend of Rosetta Impharmatics in Kirkland, Wash.,
agreed that a benefit of such analysis is the information that
could lead eventually to the design of more specific drugs for
various tumors.
They were
less confident about how soon the tools could come into use. ``We
are still a long way from translating the clues provided by DNA
microarrays into diagnostic tools,'' the two wrote in a commentary
accompanying Meltzer's paper.
``Although
microarrays might eventually be the diagnostic method of choice,
they are currently too expensive for routine clinical use,'' wrote
He and Friend, who were not part of the research team.
Meltzer said
the current cost of the tests can range from $200 to $500, but
his team worked out the minimum number of genes that need to be
tested to differentiate among the four cancers.
They concluded
that testing for 96 genes was enough to tell the cancers apart,
and Meltzer said a test for that small a number would be much
less costly.
On
the Net: Nature Medicine: http://www.nature.com/nm/
Reference
Source 102
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