Read an article today about new research done to apply big data analytics against multiple cancer strains to identify key control mechanisms that allow cancer to survive in the body and multiply. The article Big data analysis find cancer’s key vulnerabilities discusses their discovery of 24 “master regulators” that are present in a number of different cancers. The original research article is in Cell (behind paywall) but I managed to find a preprint on BiorXiv.
From a (software) coding perspective, it’s almost like a majority of cancers are re-using the same modules to perform functions that are needed by the cancer cells. Not all cancers exhibit all master regulator blocks but all the cancers that they have examined have some of them.
The researchers examined the regulatory/signaling networks of proteins in 112 cancer cell lines. They identified 407 master regulatory proteins and further analysis showed that these protiens were associated with 24 master regulatory architectures (oncotectures). A decent laymen description of a cancer oncotecture can be found in an old (2016) Economist Article Cancer’s master criminals…
Master regulatory proteins
According to the Economist article master regulatory proteins are proteins that regulate processes in a cancer cell that cause other proteins to be made, which cause other proteins to be made, etc. which affect the way a cancer cell lives and propagates inside a body.
Biologists call these sorts of proteins transcription factors which controls the copying of DNA information into mRNA which are then taken to protein factories to create proteins from that blueprint.
The research team believe they24 master regulatory (MR) blocks, if they could be disabled somehow, would disrupt the cancer cell and ultimately eliminate that cancer from a body.
It’s almost like a DevOps script that automates the deployment of software inside the cloud. The fact that they have identified 24 master regulatory (MR blocks) architectures (sequences of proteins that are occur) that apply to a wide set of cancer tumor sub-types implies that these could be needed to regulate the functionality of these cancers. If drugs could be devised to interrupt, change or deactivate these master regulatory blocks it’s quite possible that these cancers would be eliminated.
Identifying MR Blocks using (Bio/Life Sciences) Big Data
It all starts with VIPER analysis (GitHub repo) that measures a specific proteins transcriptional activity level. In this fashion they were able to analyze the 112 tumor subtype proteome (the total complement of all proteins active in a cell). And whittle these down, using cluster analysis to those that were especially relevant for the cancer cell transcription activity.
They then used DIGGIT analysis (GitHub repo of R implementation) to identify the MR proteins and identify cellular mutations that led to them. The types of mutations can be copy number, single point or gene fusion. DIGGIT analysis can help identify which of the mutations are responsible for the protein being analyzed. The DIGGIT process is a multi-step, analytical approach to identifying candidate MR proteins.
Then using tumor checkpoint hypothesis and Bayesian analysis/integration they further ranked the MR candidate proteins. Tumor checkpoints are state transitions in the life of a cancer cell where the cell assesses its environment and then determines what actions to take next.
The tumor checkpoint hypothesis says that during the life cycle of a cancer cell it goes through various state transitions. The researchers have shown that these state transitions are managed by the MR blocks they have identified.
In the final step in their analysis, they used tumor checkpoint hypothesis and modularity with saturation & modularity analysis to identify top MR proteins and the MR blocks active in the 112 tumor subtypes.
At the end of their analysis, they had identified 24 MR blocks which solely or in some combination are present in each of the 112 tumor subtypes. If these MR blocks could be attacked by specific drugs then each of these 112 tumor subtypes could essentially be eliminated from a body or rather cure that cancer.
Photo Credit(s):
- Figure 6 G from A Modular Master Regulator Landscape Determines the Impact of Genetic Alterations on the Transcriptional Identity of Cancer Cells article in BiorXix
- Wikipedia by Kevin13
- Figure 1 A, B, & C from A Modular Master Regulator Landscape Determines the Impact of Genetic Alterations on the Transcriptional Identity of Cancer Cells article in BiorXiv
