A mechanistic, data-driven method can help reduce drug costs and treat disease


A new data-driven mechanistic approach that predicts cell types in tissue will help reduce drug costs and treat diseases that have been difficult to develop drugs for, says a West Virginia University (WVU) scientist . David Klinke, PhD, a professor in the Department of Chemical and Biomedical Engineering, has developed and tested a mechanistic approach to predict the number and function of different cell types in a particular tissue and how they change when a malignant cell acquires the ability to secrete a protein.

“Ultimately, we want to develop drugs that expand the clinical benefits of immunotherapies,” noted Klinke, who is also an adjunct assistant professor at the WVU School of Medicine and a Fellow of the Cancer Institute.

Mechanistic models have been created by hand by experts, but there are gaps in researchers’ understanding of biology, as 90% of research publications reportedly focus on just 20% of genes in humans. The research from this study, “Data-Driven Learning How Oncogenic Gene Expression Locally Alters Heterocellular Networks,” published in Nature Communication, sifts through large data sets to predict how secretion of a gene product by a malignant cell influences other cell types in a tissue directly from the data. This complements the hand-created models that play an important role in drug development.

“Drug development increasingly relies on mechanistic modeling and simulation. Models that capture causal relationships between genetic drivers of oncogenesis, functional plasticity, and host immunity complement wet experiments. Unfortunately, the formulation of such mechanistic models at the cellular level currently relies on manual curation, which can bias the way data is interpreted or the priority of drug targets. In modeling networks at the molecular level, rules and algorithms are used to limit a priori bias in formulating mechanistic models,” the investigators wrote.

Bayesian analysis example template, vector illustration labeled graph lines. Decision-making approach to drawing evidence-based conclusions about assumptions. Relation prior and posterior beliefs. [Vector Mine/Getty Images]

“Here, we combine digital flow cytometry with Bayesian network inference to generate causal models of cellular-level networks linking increased gene expression associated with oncogenesis with alterations in stromal and immune cell subsets. from bulk transcriptomic datasets.We predict how increasing cellular communication network factor 4, a secreted cell matrix protein, alters the tumor microenvironment using data from patients diagnosed with breast cancer and melanoma The predictions are then tested using two immunocompetent mouse models for melanoma, which provide consistent experimental results.

“Under normal conditions, the immune system defends itself against infectious diseases,” Klinke said. “However, most cancers arise through an evolutionary process of mutation and selection. Each cell has the blueprint in its DNA to make each gene product. In this process of mutation and selection, the re-expression of some of these gene products can provide malignant cells with the ability to suppress the immune response.

Human tissues are made up of specialized cell types that are organized to maintain their function in a changing environment. Ultimately, the functional orientation of cell types within a tissue interact to create a heterocellular network, which is important for creating and maintaining tissue balance.

While researchers know that tissue balance is disrupted during oncogenesis or tumor development, there is no clear understanding of how genetic alterations influence the heterocellular network in human tissues.

Klinke pointed out that one of the barriers to expanding clinical benefits is that malignant cells create environments that suppress host immunity. This new data-driven approach allows researchers to predict how a gene product secreted by a malignant cell alters the prevalence and functional orientation of other cell types in a human tissue.

Klinke said it’s difficult to study how one event causes another in systems where it’s hard for researchers to see what’s happening, such as in intact human tissue.

To test their predictions, using digital flow cytometry and Bayesian network inference, Klinke and his team examined immunocompetent mouse models of cancer. Using this approach, Klinke was able to predict how a protein secreted by malignant cells alters the heterocellular network in melanoma and breast cancer.

Digital cytometry, which is the measurement of cell numbers and characteristics, and Bayesian network inference (a probabilistic graphical model) were used because there are datasets available with these models that contain homogenized tumor tissues ( similar) 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 changes in the same way as predicted by the Bayesian network model,” explained Klinke, who added that the conventional approach to predicting the functional orientation of cell types is to alter the expression of a secreted protein and then quantify different cell types using different experimental approaches.

For this study, Klinke used mechanistic modeling to represent the mechanisms that support biology and predict scenarios using simulation instead of actually testing the scenario in humans.

“These models are very complicated, but let me use a simple analogy,” Klinke said. “Let’s say we want to hit a target with an artillery shell and we only have one shot. Given our understanding of the laws of physics, we know we need to know some things on the projectile and all the forces acting on the projectile Given this information, we can simulate with a computer that if we fire the projectile in a certain direction or at a certain angle, it will land in a certain place.

“Similarly, we know a lot about the underlying biology associated with a drug, but there are also some things we don’t know, and we can’t test everything in humans. Given the common media conversations about high drug prices, testing new drugs in humans is expensive, and the vast majority of new drugs tested don’t work.

Klinke said one of the ways mechanistic modeling and simulation can help is by providing a way to bring all the different pieces of understanding together in the same context.

“If key aspects are missing, we run simulations to see if it makes sense to target an aspect of biology with a drug. Mechanistic modeling and simulation has impacted a number of other industries, and this is now being applied to drug development.

Klinke hopes this research can be used in other contexts such as cancer or immunological diseases.

“At the end of the day, we all care that when we get sick there are treatments that can improve our health and not bankrupt us,” he said. “Like many other industries, the pharmaceutical industry is increasingly turning to mechanistic modeling and simulation to better prioritize potential targets and reduce consultation time. Collectively, this will help reduce drug costs and treat diseases for which it was difficult to develop drugs.”


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