For their trick, instead of looking for the actual biological response to ICB treatment, the researchers picked out several substitute immune responses from the same datasets. Despite not being the primary response to ICB, together they could be used as an indicator of the effectiveness of ICB.
Thanks to this approach, the team could use a large public dataset with thousands of patient samples to robustly train machine learning models.
“A significant challenge with this work was the proper training of the machine learning models. By looking at substitute immune responses during the training process, we were able to solve this,” says Lapuente-Santana.
With the machine learning models in place, the researchers then tested the accuracy of the model on different datasets where the actual response to ICB treatment was known. “We found that overall, our machine learning model outperforms biomarkers currently used in clinical settings to assess ICB treatments,” says Eduati.
But why are Eduati, Lapuente-Santana, and their colleagues turning to mathematical models to solve a medical treatment problem? Will this replace the doctor? “Mathematical models can provide a big picture of how individual molecules and cells are interconnected, while at the same time approximate the behavior of tumors in a particular patient. In clinical settings, this means that immunotherapy treatment with ICB can be personalized to a patient. It’s important to remember that the models can help doctors with their decisions on the best treatment, they won’t replace them.” says Eduati.
In addition, the model also helps in understanding which biological mechanisms are important for the biological response. Understanding and identifying the mechanisms that mediate ICB response can guide how best to combine ICB with other treatments to improve its clinical efficacy. However, this will first require experimental validation of the identified mechanisms before translating these results to clinical settings.
Credit: Google News