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Carlo Croce Talks About Expression of miRNA in Cancer


At A Glance

Carlo Croce

1991-Present — Director, Kimmel Cancer Institute/Kimmel Cancer Center, Thomas Jefferson University, Philadelphia.

2002 - Present — Professor of Medical Oncology, University of Ferrara, Ferrara, Italy

1988 -1991 — Director, Fels Institute for Cancer Research and Molecular Biology, Temple University School of Medicine, Philadelphia.

1980 - 1988 — Associate Director, Wistar Institute of Anatomy and Biology, Philadelphia.

Education: 1969 — MD, University of Rome, Italy


Recently a team of researchers led by Carlo Croce, director of the Kimmel Cancer Center at Thomas Jefferson University in Philadelphia, published a study in the Proceedings of the National Academy of Sciences looking at the expression of genes that encoded microRNAs thought to be involved in the development of cancer. They designed and created their own microarray and compared the expression of miRNA genes in human B-cell chronic lymphocytic leukemia samples with that of normal white blood cells. They found that chromosomal region 13q14 contained two small microRNA genes that are turned off in roughly 60 percent of CLL cases. The researchers hope that by identifying the expression patterns of these genes, physicians may one day be able to use this information to diagnose and predict whether or not an individual has an aggressive form of B-cell CLL.

When did you start working with microarray technology?

I started with microarrays years ago, with micro-RNAs about two and a half years ago.

How did you get started working with microRNAs?

We were studying a disease called chronic lymphocytic leukemia. I am a cancer geneticist and I was trying to define genes on [chromosomal region] 13q14 that may be altered in CLL. Then I used some CLL with translocations and division, and I was able to find out that really the translocation break point and some small division involved precisely the region where there were two microRNA genes, near 15 and 16. And that was the first demonstration of the involvement of microRNA genes in human cancer. Now we are looking at many different cancers. Since we wanted to find out quickly which alteration in the microRNA gene might be present in human cancer, we developed some chips that contain all human and all mouse microRNA genes, and that allows us to determine which microRNA genes are up and which are down in a large number of human tissues.

Did a commercial manufacturer make a custom chip for your research?

No. We make our own chips. They are all oligonucleotide chips. We look at the precursor of the microRNA gene and at the mature product, and in our chip we have both the precursor and mature product.

How long did it take to make the chip and what platform did you run the chip on?

It took only a few months to make the chip. We have our own platform. We put the oligonucleotides on glass slides and then we hybridize it, etc.

What other cancers can you apply this to?

We have already applied this to mainly leukemia and lymphomas, lung and breast cancer, and we are doing a few others.

Tell us about the findings of your study that were recently published in Proceedings of the National Academy of Sciences.

We now have a standard because the study was only about 40 cases. Now, we have done 50 more. The bottom line is that there are signatures for the expression of microRNA genes that can predict the behavior of the leukemia. In addition, they correlate beautifully with the mutation of the variable region in the genome of immunoglobulin, which is a good prognostic indicator.

What are the implications for these findings?

The implication is that microRNA genes are clearly involved in CLL and that a number of them might be involved in the pathogenesis of the disease. Now we are looking at targets of these microRNAs. It really is the first demonstration of the correlation between a pattern of expression of microRNA genes and any human cancer.

What advantages does the microarray you’ve created offer researchers?

It’s much more simple and less costly than using variable region mutations that can be done only in very sophisticated labs, and it’s very predictive.


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