Tuesday, January 6, 2015
A statistical model has been created that measures the proportion of cancer incidence, across many tissue types, caused mainly by random mutations that occur when stem cells divide. By this measure, two-thirds of adult cancer incidence across tissues can be explained primarily by “bad luck,” when these random mutations occur in genes that can drive cancer growth, while the remaining third are due to environmental factors and inherited genes.
Scientists from the Johns Hopkins Kimmel Cancer Center have created a statistical model that measures the proportion of cancer incidence, across many tissue types, caused mainly by random mutations that occur when stem cells divide. By their measure, two-thirds of adult cancer incidence across tissues can be explained primarily by "bad luck," when these random mutations occur in genes that can drive cancer growth, while the remaining third are due to environmental factors and inherited genes.
"All cancers are caused by a combination of bad luck, the environment and heredity, and we've created a model that may help quantify how much of these three factors contribute to cancer development.
"Cancer-free longevity in people exposed to cancer-causing agents, such as tobacco, is often attributed to their 'good genes,' but the truth is that most of them simply had good luck," adds Vogelstein, who cautions that poor lifestyles can add to the bad luck factor in the development of cancer.
Researchers claim 65% of cancer cases are a result of random DNA mutations, while the remaining 35% can be explained by a combination of these mutations and environmental and hereditary factors. The implications of their model range from altering public perception about cancer risk factors to the funding of cancer research, they say. "If two-thirds of cancer incidence across tissues is explained by random DNA mutations that occur when stem cells divide, then changing our lifestyle and habits will be a huge help in preventing certain cancers, but this may not be as effective for a variety of others," says bio-mathematician Cristian Tomasetti, Ph.D., an assistant professor of oncology at the Johns Hopkins University School of Medicine and Bloomberg School of Public Health. "We should focus more resources on finding ways to detect such cancers at early, curable stages," he adds.
To sort out the role of such random mutations in cancer risk, the Johns Hopkins scientists charted the number of stem cell divisions in 31 tissues and compared these rates with the lifetime risks of cancer in the same tissues among Americans. From this so-called data scatterplot, Tomasetti and Vogelstein determined the correlation between the total number of stem cell divisions and cancer risk to be 0.804. Mathematically, the closer this value is to one, the more stem cell divisions and cancer risk are correlated.
"Our study shows, in general, that a change in the number of stem cell divisions in a tissue type is highly correlated with a change in the incidence of cancer in that same tissue," says Vogelstein. One example, he says, is in colon tissue, which undergoes four times more stem cell divisions than small intestine tissue in humans. Likewise, colon cancer is much more prevalent than small intestinal cancer.
"You could argue that the colon is exposed to more environmental factors than the small intestine, which increases the potential rate of acquired mutations," says Tomasetti. However, the scientists saw the opposite finding in mouse colons, which had a lower number of stem cell divisions than in their small intestines, and, in mice, cancer incidence is lower in the colon than in the small intestine. They say this supports the key role of the total number of stem cell divisions in the development of cancer. Using statistical theory, the pair calculated how much of the variation in cancer risk can be explained by the number of stem cell divisions, which is 0.804 squared, or, in percentage form, approximately 65 percent.
Finally, the research duo classified the types of cancers they studied into two groups. They statistically calculated which cancer types had an incidence predicted by the number of stem cell divisions and which had higher incidence. They found that 22 cancer types could be largely explained by the "bad luck" factor of random DNA mutations during cell division. The other nine cancer types had incidents higher than predicted by "bad luck" and were presumably due to a combination of bad luck plus environmental or inherited factors.
"We found that the types of cancer that had higher risk than predicted by the number of stem cell divisions were precisely the ones you'd expect, including lung cancer, which is linked to smoking, skin cancer, linked to sun exposure, and forms of cancers associated with hereditary syndromes," says Vogelstein.
"This study shows that you can add to your risk of getting cancers by smoking or other poor lifestyle factors. However, many forms of cancer are due largely to the bad luck of acquiring a mutation in a cancer driver gene regardless of lifestyle and heredity factors. The best way to eradicate these cancers will be through early detection, when they are still curable by surgery," adds Vogelstein.
Saturday, December 27, 2014
A computer-aided design tool was created for genetic languages to guide the design of biological systems known as GenoCAD, the open-source software was developed by researchers at the Virginia Bioinformatics Institute at Virginia Tech to help synthetic biologists capture biological rules to engineer organisms that produce useful products or health-care solutions from inexpensive, renewable materials.
GenoCAD helps researchers in the design of protein expression vectors, artificial gene networks, and other genetic constructs, essentially combining engineering approaches with biology.
Synthetic biologists have an increasingly large library of naturally derived and synthetic parts at their disposal to design and build living systems. These parts are the words of a DNA language and the "grammar" a set of design rules governing the language.
GenoCAD is an open-source computer-assisted-design (CAD) application for synthetic biology. The foundation of GenoCAD is to consider DNA as a language to program synthetic biological systems. GenoCAD includes a large database of annotated genetic parts which are the words of the language. GenoCAD also includes design rules describing how parts should be combined in genetic constructs. These rules are used to build a wizard that guides users through the process of designing complex genetic constructs and artificial gene networks. The same rules are used by the GenoCAD compiler to maintain the integrity of existing constructs. GenoCAD provides users with data import and export capabilities using standard formats (FASTA, GenBank, and tab-delimited text) so that users' personal workspaces can be customized to meet their specific needs. It has to be expressive enough to allow scientists to generate a broad range of constructs, but it has to be focused enough to limit the possibilities of designing faulty constructs.
"Just like software engineers need different languages like HTML, SQL, or Java to develop different kinds of software applications, synthetic biologists need languages for different biological applications," said Jean Peccoud, investigator of the GenoCAD project. "From its inception, we envisioned GenoCAD as a framework allowing users to capture their expertise of a particular domain in languages that they could use themselves or share with others."
The researchers said encapsulating current knowledge by defining standards will become increasingly important as the number and complexity of components engineered by synthetic biologists increases.
They propose that grammars are a first step toward the standardization of a broad range of synthetic genetic parts that could be combined to develop innovative products.
"Developing a grammar in GenoCAD is a little like writing a review paper," Purcell said. "You start with the headings and you progressively dig deeper in the details. At the end of the process, you have a much better appreciation for what you know and what you don't know about a particular domain."