Computer Model Predicts Response to Immunotherapy

A group of researchers at UPF and the Marchuk Institute of Numerical Mathematics have designed a mathematical model to predict the response of HIV-infected individuals to a type of cancer immunotherapy. The study was published yesterday in Computational Biology.

More than thirty years after its identification, HIV infection is managed with multiple antiretroviral drugs. Thanks to the current therapy regimens, in many parts of the world, HIV is a chronic condition for which progression to AIDS is increasingly uncommon. However, many long-term HIV patients develop cancer due to constant immune activation.

Due to their central role in the immune response, T cells play a central role in both diseases. When the immune system is active for extended periods—such as with persistent viral infections or the progressive development of cancer—T cells receive an excessive amount of signals, which lead them to suffer functional deterioration, a phenomenon known as T cell exhaustion.

Exhausted T cells can be reactivated by a type of immunotherapy known as checkpoint inhibitors. This type of immunotherapy increases T cell proliferation and function, which reinvigorates the immune response against tumors. In this study, researchers generated models to assess how well HIV patients would respond to this therapy.

The scientists studied human blood samples from HIV-infected individuals, stimulated them with checkpoint inhibitors, and then monitored the behavior in cell culture. “Then we quantified the different responses, and developed a mathematical model to predict how the progression of HIV will be affected,” says coauthor Gennady Bocharov. “We predicted how much the virus is reduced and how many CD4 T cells increase in the patient, which has a clinical significance.”

From the model, the scientists predict that most HIV-infected individuals will benefit from checkpoint inhibitor therapy, but the benefit is dependent on intrapatient immune responsiveness.

“The immune response to HIV differs between individuals, and this can determine whether the person experiences more, less, or no benefit from the therapy,” explains coauthor Andreas Meyerhans. Their main prediction is that the individuals who already control HIV well do not benefit so much from this therapy, but individuals who do not control the virus well may benefit more.

“Our general approach can be extended to other immunotherapies and is an important step forward towards personalized treatment strategies in infectious diseases and cancers,” Bocharov concludes.