Genetic Signatures that Distinguish Cancer and Non-cancer Patients

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A group of researchers led by scientists from the Virginia Bioinformatics Institute (VBI) at Virginia Tech have developed a new technology that detects distinct genetic changes differentiating cancer patients from healthy individuals [1]. The technology is described in a recent study published in the journal Genes, Chromosomes and Cancer and may one day serve as the basis for a cancer predisposition test.

The majority of DNA in cells is non-coding, “junk DNA“, meaning that it isn’t transcribed into protein. The largest amount of non-coding DNA consists of microsatellites — specific repeated sequences of one to six nucleotides within the genome. There are over 2 million satellite repeats in the human genome. They tend to vary greatly among individuals and have traditionally been used in forensics and paternity tests.

In December 2010, VBI researchers discovered a four-nucleotide repeat (AAAG) in the estrogen-related receptor gamma (ESRRG) gene, which indicates an individual’s genetic susceptibility to breast cancer [2]. Longer DNA sequences of the repetitive microsatellite were much more likely to be present in breast cancer patients than healthy volunteers; patients with a greater number of copies of the repeat in the promoter region of the ESRRG gene have a 3-fold higher cancer susceptibility rate than those who do not.

In the present study, instead of focusing on a single gene, the scientists created a design for a new DNA microarray that allowed them to measure the over 2 million microsatellites in the human genome in a single experiment. They evaluated the global microsatellite content in the genomes of 72 cancer, cancer-free, and high risk patient and cell line samples.

A unique, reproducible and statistically significant motif of 18 pattern-specific microsatellite families was identified in germline and tumor DNA from breast cancer patients but not in germline DNA of cancer-free patients or in breast cancer patients with BRCA1 or BRCA2 mutations.

Germline DNA: the genetic material passed from parent to child. Gametes — a cell that fuses with another cell during fertilization in organisms that reproduce sexually — such as the sperm or egg are part of the germline. Cells that are not in the germline are called somatic cells.

These 18 pattern-specific microsatellite families suggest a new mechanism disrupting the genome in cancer patients and may represent a new breast cancer risk biomarker.

The repetitive motifs were also more pronounced in the germlines and tumors of colon cancer tumor patients (3/6 samples) and microsatellite unstable colon cancer cell lines. Although there were only 9 colon cancer samples, it suggests a more general role for microsatellites in the genome. The pattern on the microarray serves as the biomarker that can measure the amount of risk an individual has for developing cancer in the future.

Harold “Skip” Garner, VBI executive director who leads the institute’s Medical Informatics and Systems Division, explained:

We have now arrived at a new biomarker — an indicator that could be used to evaluate the amount of risk that you have for developing cancer in the future. This is part of an effort to understand their (microsatellite) role in the genome and then proceed on directly towards something that is of utility in the clinic. What just came out in our paper is a description of the technology that allows us to very quickly and efficiently and inexpensively measure these two million places using a uniquely designed microarray … It’s the pattern on that microarray that provides us the information we need.

You can watch an interview with Dr. Garner discussing the research and its future implications below:

References

  1. Galindo et al. Sporadic breast cancer patients’ germline DNA exhibit an AT-rich microsatellite signature. Genes Chromosomes Cancer. 2011 Apr;50(4):275-83. doi: 10.1002/gcc.20853. Epub 2011 Jan 14.
    View abstract
  2. Genetic Biomarker for Risk of Breast Cancer Identified in “Junk” DNA. Biomarker Commons. 2010 Dec 20.
About the Author

Walter Jessen, Ph.D. is a Data Scientist, Digital Biologist, and Knowledge Engineer. His primary focus is to build and support expert systems, including AI (artificial intelligence) and user-generated platforms, and to identify and develop methods to capture, organize, integrate, and make accessible company knowledge. His research interests include disease biology modeling and biomarker identification. He is also a Principal at Highlight Health Media, which publishes Highlight HEALTH, and lead writer at Highlight HEALTH.