Delving Into ‘Kinome,’ Howard Hughes Team Links Mutations to Colon Cancer
Research into all known tyrosine kinase enzymes has led a team of scientists at the Howard Hughes Medical Institute to identify gene mutations found in roughly one out of every three patients with colon cancers. If verified, their work may spur drug companies to develop molecular diagnostic products and therapeutics to target these mutations.
It is widely known that abnormal activity of tyrosine kinases plays a role in certain kinds of tumorogenesis. Using this knowledge, the Howard Hughes team, led by Bert Vogelstein and Sanford Markowitz, screened genes known to produce cancer-causing tyrosine kinases.
First, to reduce the amount of sequencing they would need to do, the researchers focused their search on mutations in the kinase domain of tyrosine kinases and “related enzymes,” which they said are “principally responsible” for enzymatic activity.
Markowitz, a researcher at Case Western Reserve University and University Hospitals of Cleveland, and Vogelstein, a researcher at the Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, speculated that if there were going to be mutations that “constitutively activated” these enzymes — and thus could be targeted by drugs — the kinase domains “would be the ones to go for,” said Vogelstein.
The researchers first identified the kinase domains of 138 tyrosine kinases and similar enzymes from their kinase database, which they dubbed the ‘kinome.’ Next they extracted these same domains from 35 colorectal cancer cell lines and sequenced them for comparison. Their research, published in the May 9 Science, uncovered mutations within the kinase domains of 14 genes.
Markowitz and Vogelstein went on to analyze 147 additional colorectal cancer cell lines for kinase-domain mutations in these 14 genes, and then sequenced the entire coding region of all mutated kinases. In all, the researchers discovered 46 new mutations.
“We envision that in the future… each patient with colon cancer could have a diagnostic analysis to determine which kinases are activated by mutation — an easy task once you know which ones to look for,” said Vogelstein. “Then, that patient could be treated with a drug that specifically targets that kinase.”
Markowitz was more reserved. “Two of the major questions to be addressed … are the functional consequences of the mutations we are finding, and whether these kinases are targets for drugs.” he said.
International Team Builds Computational Model that Uncovers Regulatory Genes
A team of researchers in the United States and Israel said they have developed a computational method to detect regulatory genes.
In fact, the scientists, from Stanford University, Hebrew University, and the Weizmann Institute in Israel, said they have used the method to uncover “several previously unknown” regulatory genes in Saccharomyces cerevisiae.
Traditionally, researchers identify regulatory genes experimentally rather than computationally. The new method, in contrast, was designed not only to identify regulatory candidates but also to predict how each gene might affect cellular activity.
The team found “several possible new” regulatory genes in S. cerevisiae together with the clusters they regulate, and the researchers claim to have confirmed three of the predictions in the lab.
The new approach, described online May 12 in Nature Genetics, can also identify clusters but is believed to be the first method to incorporate data about known and putative regulatory genes, and the first to “simultaneously predict” which gene or genes regulate each cluster, according to the researchers.
Spearheaded by Daphne Koller, a computer science professor at Stanford, the pattern-recognition technique “builds on statistical models and the widely used technology of relational databases” to look for patterns across many sources, including microarray data, DNA-sequence data, and protein-protein-interaction data, the researchers said.
“The generality of the method lets the researchers assemble data sets like Lego blocks, plugging a new database into the relational structure and letting the algorithm go to work,” they said in a statement last week.
Koller and her colleagues have developed a visualization tool called GeneXPress to help researchers manipulate these databanks. The software is freely available at: http://genexpress.stanford.edu/]
“Knowing the control mechanism for gene clusters is crucial for understanding how cells respond to internal and external signals,” team member David Botstein, a professor of genetics at Stanford, said in a statement released by Stanford last week. “This new computational method efficiently generates targets for testing and proposes hypotheses about their regulatory roles that can be experimentally confirmed.”