The researchers say it’s a first step toward a more useful classification of lung cancers that could yield sharper diagnoses and better treatments for the number one cancer killer.

Because the technique groups tumors by the “off” and “on” patterns of tens of thousands of genes in the cancer cells, the scientists could capture distinctive genetic “signatures” of each cancer type. One such signature identified a type of tumor that on average would kill the patient nearly 1 ½ years earlier than a similar type with a different signature. Scientists also used the genetic signatures to distinguish between tumors originating in the lung and those that had spread from elsewhere in the body – a distinction that can be impossible to make with current methods.

“We’ve been able for the first time to develop a new biological classification” within adenocarcinomas, the most common lung cancers, “that we think will be clinically relevant” to treatment decisions, said Matthew Meyerson, MD, PhD, an assistant professor of pathology at Dana-Farber and Harvard Medical School. He is the senior author of the paper being posted online Nov. 13 by the Proceedings of the National Academy of Sciences. The report will appear in the journal’s Nov. 20 print edition.

The first author is Arindam Bhattacharjee, PhD, a postdoctoral fellow in Meyerson’s laboratory. Other authors include William G. Richards, PhD, Todd R. Golub, PhD, of Pediatric Oncology and also at the Whitehead Institute for Biomedical Research in Cambridge, and David J. Sugarbaker, MD, at Brigham and Women’s Hospital and Dana-Farber.

Compared with cancers of the blood, for example, lung tumors are more difficult to assign to various categories, says Meyerson, and even experienced pathologists often disagree. The problem is that there are fewer unique “markers,” or distinguishing traits that can be easily identified, in lung cancers than in leukemia or lymphoma. The same difficulty faces specialists trying to identify the basic characteristics of most solid tumors.

In the lung cancer experiment, the researchers obtained 203 lung tissue samples, 139 of which were adenocarcinomas. To determine which genes were active and which were silent, the researchers applied RNA extracts from the samples on to the gene chips. On each “chip” or “microarray” – a piece of glass the size of a fingernail – portions of 12,600 known genes were tethered at one end, floating free so that pieces of RNA from the tumor samples could bind with them. (The RNA pieces were copies of the DNA sequences from the tumor samples. They were labeled with a fluorescent chemical that would reveal their presence when the chip was “read” by a computer.)

The RNA from the cancer samples binds to an identical gene sequence on the chip, thus identifying the RNA and the gene it came from. The computer also records the intensity of the fluorescence in the RNA, which is a measure of how actively the gene is being expressed – that is, functioning to make a protein.

After complex mathematical analyses, the tumor samples’ genetic profiles can be compared side-by-side in a visual display. Those with similar genetic off-on patterns are assigned to similar categories. In general, the major categories of lung cancers defined by the gene analysis corresponded to the traditional pathological categories, said Golub.

However, “we also found evidence for subtypes [of adenocarcinomas] that are currently not recognized routinely,” he said. So far, they are only identified as C1, C2, C3 and C4. Each type is characterized by different groups of genes that are very active or markedly inactive. And biologically, the four types had differing behavior.

For example, patients who had the C2 adenocarcinoma had worse survival than those with any of the other three: their median survival was 21 months compared with 40.5 months for C1, C3 or C4. The categorizing also identified tumors that were less associated with smoking than others.

Because the analysis reveals which genes are most active in the different tumors, the researchers envision future drugs tailored to target one or more genes in each tumor, while leaving normal cells unharmed. None of the hoped-for benefits are close at hand, however.

“I want to emphasize that none of this is at the point of clinical use and these tests are not being currently done for patients,” said Golub. “We want to be very cautious not to alter the current standard of care until we’re very sure” that the new classification is superior.

The work was funded by the National Cancer Institute and also by Millennium Pharmaceuticals, Affymetrix and Bristol-Myers Squibb.

Dana-Farber Cancer Institute (www.dana-farber.org) is a principal teaching affiliate of the Harvard Medical School and is among the leading cancer research and care centers in the United States. It is a founding member of the Dana-Farber/Harvard Cancer Center (DF/HCC), a designated comprehensive cancer center by the National Cancer Institute.

Note: If no author is given, the source is cited instead.

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