Tuesday , October 26 2021

A new mathematical tool for studying cancer-related genetic interactions


Biologists at Cold Spring Harbor (CSHL) use a mathematical approach developed in the laboratory of CSHL assistant professor David McCandlish to find solutions to a variety of biological problems. Originally created as a way to understand the interactions between different mutations in proteins, McCandlish and his colleagues now use it to learn about the complexities of gene expression and chromosomal mutations associated with cancer.

This is one of the things that is really fascinating in mathematical research, sometimes you can see connections between topics that seem so different at first glance, but on a mathematical level they may use some of the same technical ideas. “

David McCandlish, assistant professor of CSHL

All of these questions involve mapping the probabilities of different variations on a biological theme: which combinations of mutations are most likely to occur in a particular protein, for example, or which chromosomal mutations are most commonly found together in the same cancer cell. McCandlish explains that it is a problem in estimating density — a statistical tool that predicts how often an event occurs. Estimating density can be relatively simple, such as plotting different heights in a group of people. But when we deal with complex biological sequences, such as hundreds or thousands of amino acids, strung together to form proteins, predicting the probabilities of each potential sequence becomes surprisingly complex.

McCandlish explains the fundamental problem his team uses to solve math:

“Sometimes if you make, say, one mutation in a protein sequence, it doesn’t do anything. Proteins work fine. And if you make another mutation, it still works fine, but then if you put them together,” now you have a broken protein. We tried to design methods to model not only interactions between pairs of mutations, but between three or four or any number of mutations. “

The methods they have developed can be used to interpret data from experiments that measure how hundreds of thousands of different combinations of mutations affect protein function.

This study, published in the Proceedings of the National Academy of Sciences, began with conversations with two CSHL colleagues: CSHL associate Jason Sheltzer and Associate Professor Justin Kinney. They worked with McCandlish to use his methods in gene expression and the development of cancer mutations. The software, released by the McCandlish team, will allow other researchers to use these same approaches in their work. He says he hopes it will be used for a variety of biological problems.


Cold Spring Harbor Laboratory

Magazine reference:

Chen, WC, et al. (2021) Estimation of theoretical field density for biological sequence space with applications for 5 ′ compound diversity and aneuploidy in cancer. PNAS. doi.org/10.1073/pnas.2025782118.

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