Study examined how to better detect lung cancer that spreads to the brain
A team of scientists at the Translational Genomics Research Institute (TGen) won a $1,000 prize for best scientific paper presented at BIBM09, a premier bioinformatics and biomedicine conference.
The paper, Identifying MiRNA and Imaging Features Associated with Metastasis of Lung Cancer to the Brain, was selected as the best from among 233 scientific submissions at the IEEE (Institute of Electrical and Electronics Engineers) International Conference on Bioinformatics & Biomedicine, held Nov. 1-4 in Washington, D.C.
The paper was selected because of the amount of work done for the study, including laboratory work, and the novelty of the research approach, conference judges said: "We (computational people) often focus too much on the elegance of computational modeling. This is a good paper to reach out to the computational community with a work by a large, collaborative team with a new idea."
The study - funded in part by the IBIS Foundation of Arizona, Science Foundation Arizona, and the National Institutes of Health - focused on using microRNAs, small molecules that regulate gene expression in cells, to help understand and predict how malignant lung cancer often spreads to the brain.
MicroRNAs and imaging characteristics on scanning devices - CT (computerized axial tomography), and PET (positron emission tomography) -were used as biomarkers that could indicate the presence of metastatic brain tumors, also known as brain metastases.
In addition, investigators used an in-silico conditioning algorithm, based on a mathematical model for contextual genomic regulation, to further identify biomarkers and validate their findings.
"If such markers could be detected by non-invasive means, such as with PET/CT, it could potentially revolutionize personalized healthcare in this country,'' the paper said.
Nearly 25 percent of lung cancer patients will develop brain metastases, but there are no good measures to identify those at high risk. In certain situations, radiation treatment can reduce this risk, but this treatment can also have negative side effects. In general, clinicians treat brain metastases when these cause symptoms and are visible on scanning devices. Researchers posited that such treatment and its side effects could be avoided among some cancer patients if clinicians were better able to identify those patients who will develop brain metastasis.
Dr. Seungchan Kim, the paper's senior author who developed the in-silico conditioning algorithm, said the study could have "high impact'' in predicting lung cancer spreading to the brain. "The results are promising and warrant further evaluation and additional validation," said Dr. Kim, an Investigator and Head of the Biocomputing Unit in TGen's Computational Biology Division and an Assistant Professor at Arizona State University's School of Computing, Informatics and Decision Systems Engineering.