A data-driven approach to helping reduce drug costs
“Ultimately, we want to develop drugs that extend the clinical benefits of immunotherapy,” said Klinke, who is also an assistant professor at the WVU School of Medicine and a member of the Cancer Institute.
The mechanical models have been created by hand by experts, but there is still a gap in the understanding of biological researchers because 90% of research publications focus on only 20% of human genes.
Research from this study, published in Nature Communications, sifting through large data sets to predict how a malignant cell secretes a gene product that affects other cell types in tissue directly from the data. This provides an addition to the hand-created models that are important in drug development.
“Under normal circumstances, a person’s immune system fights off an infectious disease,” says Klinke. “However, most cancers arise through an evolutionary process of mutation and selection. Each cell has a blueprint in its DNA to make every gene product. with the ability to suppress the immune response.”
Human tissues are made up of specialized cell types that are organized to maintain function in a changing environment. Finally, the functional orientation of cell types in a tissue interact to create a heterocellular network – a network of many different cell types that interact to achieve a common goal. A heterocellular network is important for the generation and maintenance of tissue equilibrium.
While researchers know that tissue equilibrium is disrupted during carcinogenesis or tumor growth, there is still no clear understanding of how genetic changes affect the heterogeneous network. cells in human tissues.
One of the barriers to expanding clinical interest, Klinke said, is that malignant cells create an environment that inhibits host immunity.
Klinke says studying how one event causes another is a difficult challenge to do in systems where it can be hard for researchers to see what’s going on – like in an intact human tissue. .
To test their predictions, using digital cytometry and Bayesian network inference, Klinke and his team examined mouse models of cancer immunity. With this approach, Klinke was able to predict how a protein secreted by malignant cells alters the heterocellular network in the context of melanoma and breast cancer.
Digital cytometry, which is a measurement of the number and characteristics of cells, and Bayesian network inference (a graphical probabilistic model) were used because of the availability of datasets with these models containing Homogeneous (similar) tumor tissues are sequenced.
“We can change the expression of a gene and then see if the prevalence and functional orientation of different cell types in the tumor change similarly as predicted by the Bayesian network model. or not.”
The conventional approach to predicting the functional orientation of cell types is to alter the expression of a secreted protein and then quantify the different cell types using different methods, Klinke said. different experiments.
For this study, Klinke used a mechanistic model to represent biological support mechanisms and predicted scenarios using simulations rather than actually testing the scenario in humans.
“These models are very complex but let me use a simple analogy,” Klinke said. “Let’s say that we want to hit the target using shells and we only have one shot. With our understanding of the laws of physics, we know that we need to know a few things. thing about ballistics and all the forces acting on the projectile With this information we can simulate by computer that if we fire the bullet in a certain direction or an angle, it will land at a certain position. certain mind.
“Similarly, we know a lot about the basic biology involved in a drug, but there are also some things we don’t know and we can’t test everything in humans. drugs, testing for drugs. New drugs in humans are expensive, and the vast majority of new drugs tested don’t work.”
Klinke says that one of the ways that mechanical modeling and simulation can help is by providing a way to bring all the disparate pieces of understanding together in the same context.
“If key aspects are missing, we run simulations to see if targeting certain aspects of biology with drugs makes sense. Modeling and mechanistic simulations have had an impact on some. other industries and this is now being applied to drug development.”
Klinke hopes that the research could be used in other contexts such as cancer or immune diseases.
“Ultimately, we all care that when we are sick, there are treatments that can improve our health and not bankrupt us in the process. Like many industries In other industries, the pharmaceutical industry is increasingly turning to mechanical modeling and simulation to better prioritize potential targets and reduce time to clinic. drugs and help treat diseases where drugs are difficult to develop.”
Citation: Data-driven learning how locally expressed oncogenes alter heterocellular networks