AI Tool Predicts Immunotherapy Response Using Routine Blood Test

A new tool may better predict whether individual cancer patients will benefit from immune checkpoint inhibitors — a type of immunotherapy — using only routine blood tests and clinical data. The artificial intelligence–based model, dubbed SCORPIO, was developed by a team of researchers from Memorial Sloan Kettering Cancer Center (MSK) and the Tisch Cancer Institute at Mount Sinai.

The model is not only cheaper and more accessible, it’s significantly better at predicting outcomes than the two current biomarkers approved by the US FDA, according to findings published in Nature Medicine.

“Immune checkpoint inhibitors are a very powerful tool against cancer, but they don’t yet work for most patients,” says study co-senior author Luc Morris, MD, a surgeon and research lab director at MSK. “These drugs are expensive, and they can come with serious side effects.”

So, the key is patient selection — matching the drugs with patients who are most likely to benefit, Dr Morris says.

“There are some existing tools that predict whether tumors will respond to these drugs, but they tend to rely on advanced genomic testing that is not widely available around the world,” he adds. “We wanted to develop a model that can help guide treatment decisions using widely available data, such as routine blood tests.”

Checkpoint inhibitors target the immune system rather than the cancer itself. These drugs work by taking the brakes off immune cells, allowing them to better fight cancer. MSK clinicians and scientists played a key role in bringing the new class of drugs to patients.

The new study was jointly overseen by Dr Morris and Diego Chowell, PhD, an Assistant Professor of Immunology and Immunotherapy, Oncological Sciences, and Artificial Intelligence and Human Health at the Icahn School of Medicine at Mount Sinai, and a former postdoctoral fellow at MSK.

According to Dr Morris, SCORPIO was initially developed by collecting data from MSK patients. Collaborating with the team at Mount Sinai, they used a type of artificial intelligence called ‘ensemble machine learning,’ which combines several tools to look for patterns in clinical data from blood tests and treatment outcomes. The model was developed using a rich resource of retrospective data from more than 2,000 patients from MSK who had been treated with checkpoint inhibitors, representing 17 different types of cancer. The model was then tested using data from 2,100 additional MSK patients to verify that it was able to predict outcomes with high accuracy.

The model was then applied to nearly 4,500 patients treated with checkpoint inhibitors in 10 different phase 3 clinical trials from around the world. Further validation was done with additional data from nearly 1,200 patients treated at Mount Sinai. In total, the study includes nearly 10,000 patients across 21 different cancer types — representing the largest dataset in cancer immunotherapy to date.