Machine Learning Identifies Multiple Underlying Factors Predicting Response to Immunotherapy
A research team is using a branch of artificial intelligence known as machine learning to better target immunotherapy to those who will benefit. In a recent study published by Leiserson et al in PLOS One, the team used data from a clinical trial of patients with bladder cancer to demonstrate that their approach could identify a suite of features that accurately predicted a key immune system response to treatment—while reducing overtreatment by half.
Immunotherapies, which use a person’s own immune system to fight cancer, have produced revolutionary results in recent years, yet most of them only work for a minority of patients. Improving the use of immunotherapy and reducing costs may lie in more accurately predicting which patients will benefit.
Mark Leiserson, PhD, Assistant Professor of Computer Science at the University of Maryland, and colleagues from Microsoft Research and Memorial Sloan Kettering Cancer Center believe the way to better predictions lies in a new computer modeling approach, one that analyzes data on multiple facets of patients and their cancer simultaneously.
“If your goal is to treat everyone in that particular dataset who will respond, the type of multifactorial modeling we show in this paper will let you do that while treating many fewer people who won't respond," said Dr. Leiserson. “What’s also exciting about this study is that we were not just looking at patient outcome, but at a specific marker of immune response, which gave us a much better picture of what’s going on.”
Dr. Leiserson and colleagues showed that their multifactorial computer model predictions of which patients would benefit could include as few as 38% of those who did not benefit while still capturing 100% of the patients who did. The key, they found, was to include three distinct types of data, something not currently standard in cancer studies or treatments.
Although immunotherapy researchers are beginning to collect more information about patients and their responses to therapy, the focus is still largely on finding a few key markers that stand out as important predictors of success. The solution, however, may be far more complex. There may not be just a handful of important features or markers for all patients, and those that exist are likely to function in some complicated combination.
“People are realizing that predicting response is more and more appropriate and needed, and to be able to do this, the traditional kind of single biomarker approach isn’t always enough,” Dr. Leiserson said.
Methods and Findings
To generate their computer model, Dr. Leiserson and his team analyzed data from a clinical trial with a uniquely rich dataset that captured information about tumor cells, immune cells, and patient information such as demographics and medical history. Like many studies, the trial was aimed at finding key features associated with a specific response to the drug. Recognizing the potential in such a multimodal data set, the researchers saw an opportunity to apply machine learning to the problem. They fed 36 different features into their model and allowed the computer to identify patterns that could predict increases in potential tumor-fighting immune cells in a patient’s blood after treatment (in the study patients, expansion of T cells in the blood post-therapy was associated with progression-free survival).
The resulting algorithm identified 20 features that, when analyzed together, explained 79% of the variation in patient immune responses. According to Dr. Leiserson, this means that the unusually comprehensive set of features gathered for these patients is sufficient to predict the patient immune response with high accuracy.
The researchers also found that if they eliminated any one of the three categories of data from the model (tumor data, immune cell data, or patient clinical data) the immune response was no longer predictable—their model could only predict at most 23% of the variation. Dr. Leiserson stressed that it’s not necessarily the 20 characteristics that are important, but rather, the reliance on a multifactorial approach.
“These features we identified may not be the only features that can be used to predict how a patient will respond,” he said. “There may be others that you could replace these with, but it’s about the method and the inclusion of all three categories of features.”
Dr. Leiserson sees this work as a natural parallel to current efforts in precision oncology, which aims to tailor treatments to the genetics and molecular profiles of individual patients’ tumors.
“We are trying to predict what’s going to happen for a single patient by looking at their molecular profile and clinical history,” he said. “It’s about building an understanding of the molecular landscape of the tumor, which provides additional information beyond which tissue it’s in or what the tumor looks like under the microscope.”
Both the data used for the study and the algorithm Dr. Leiserson and his team developed are open source and available on Github at https://github.com/lrgr/multifactorial-immune-response.
Disclosure: The study authors' full disclosures can be found at journals.plos.org
The content in this post has not been reviewed by the American Society of Clinical Oncology, Inc. (ASCO®) and does not necessarily reflect the ideas and opinions of ASCO®.
