Scientists have developed a way to identify important cancer mutations. This approach can mode the effects that cancer mutations have on the intricate patterns of communication between groups of proteins involved in cell signaling. Using this tool, researchers can get a better understanding of how mutations can alter signaling networks.

To find meaning in the rising oceans of genomic data, scientists, from the new Laboratory of Systems Pharmacology, have created multidimensional models and applied them to the genome-wide studies. The models show that specific mutations somehow alter the social networks of proteins in cells. From this they can deduce which mutations among the numerous mutations present in cancer cells might actually play a role in driving disease.

Find driver mutations

Many of the most widely studied cancer genes, such as P53 and Ras, were discovered after decades of work by many groups. But today, in the era of high throughput genomics, there are thousands of times more data than previously existed, showing that the sheer volume of catalogued cancer mutations is vast.

Not all mutations actually influence tumor behavior. In order to distinguish the drivers, researchers use a kind of “polling” strategy in which the most common mutations–deduced as the significant ones–can be identified. Only the most promising candidates are then subjected to the detailed analysis in the fields of cancer biomarker and molecular diagnostics.

For every common mutation, there are approximately four rare ones, so, based on numbers, rare mutations might be much more significant than previously suspected. Some researchers consider a large universe of rare mutations to be dark matter. a study, published in the journal Nature Genetics ,shows that all this dark matter actually matters.

The researchers found that mutations are not really the blunt force that they expected. An altered protein cause a subtle, almost surgically precise, altering of the communication pathway, rather than knocking out an entire branch of a network or inserting an entirely new character.

From the perspective of the mutation, it is hard to be so precise, but cancer can’t be too disruptive, or else it might die. This subtle altering of networks achieves that objective. Drug companies can exploit this and possibly develop more targeted therapies.

Reference:

A multiscale statistical mechanical framework integrates biophysical and genomic data to assemble cancer networks. Nature Genetics, 2014