Running the numbers
NEW YORK—One of the rising stars in terms of cancer treatments is immunotherapy, both alone and in combination with traditional chemotherapy drugs. But not all patients respond to immunotherapies, leaving a need for methods of better determining which patients are likely to be successful on which regimens. Recent work from a team at the Icahn School of Medicine at Mount Sinai has possibly yielded an answer: a mathematical model that can predict patient response to certain immunotherapies. The study, “A neoantigen fitness model predicts tumour response to checkpoint blockade immunotherapy,” appeared in Nature Nov. 8.
“This research represents a big step forward in understanding why some tumors are more aggressive than others and being able to predict rationally which neoantigens will be the most effective at stimulating an immune response,” said Dr. Vinod P. Balachandran, a member of the David M. Rubenstein Center for Pancreatic Cancer Research at MSK. Balachandran is the corresponding author of a companion study that was published in Nature regarding how a similar model can be applied to examine immune response in pancreatic cancer patients who live longer than others.
The model was created using data from patients with melanoma and lung cancer who were undergoing treatment with immune checkpoint inhibitors. It tracks several properties tied to immune response, including neoantigens, which, as noted in the paper, are tumor-specific, mutated peptides presented on the surface of cancer cells. That is, neoantigens are specific to tumors that are mutating and growing.
“Checkpoint blockade immunotherapies enable the host immune system to recognize and destroy tumor cells,” the authors wrote. “Their clinical activity has been correlated with activated T cell recognition of neoantigens … Here we present a fitness model for tumors based on immune interactions of neoantigens that predicts response to immunotherapy. Two main factors determine neoantigen fitness: the likelihood of neoantigen presentation by the major histocompatibility complex (MHC) and subsequent recognition by T cells. We estimate these components using the relative MHC binding affinity of each neoantigen to its wild type and a nonlinear dependence on sequence similarity of neoantigens to known antigens. To describe the evolution of a heterogeneous tumor, we evaluate its fitness as a weighted effect of dominant neoantigens in the subclones of the tumor.”
“We present an interdisciplinary approach to studying immunotherapy and immune surveillance of tumors,” said senior author Dr. Benjamin Greenbaum, who is affiliated with the departments of Medicine, Hematology and Medical Oncology, Pathology, and Oncological Sciences at The Tisch Cancer Institute at the Icahn School of Medicine. “This approach will hopefully lead to better mechanistic predictive modeling of response and future design of therapies that further take advantage of how the immune system recognizes tumors.”
It’s thought that this model could also be applied in the search for new immunological therapeutic targets—specifically that “low-fitness neoantigens identified by our method may be leveraged for developing novel immunotherapies,” as noted in the paper—as well as in the design of vaccines for patients who don’t respond to immunotherapies.
This work was supported by a variety of grants, and was part of the Stand Up To Cancer “Convergence” model funding, receiving funding from several Stand Up To Cancer grants, as well as grants from other industry and nonprofit organizations.
“It is extraordinary to see the Stand Up To Cancer ‘Convergence’ model, which integrates quantitative and clinical sciences, yield results like these. The potential benefit for patients diagnosed with melanoma or cancer of the lung or pancreas is exciting because these are some of the most challenging diagnoses for patients and their doctors and this research will help give patients the best information about the options they have,” said Stand Up to Cancer President and CEO Dr. Sung Poblete. “This research has tremendous value in predicting which patients may respond to immunotherapy before treatment begins, which will help us get the right medicine to the right patient. In addition the model provides new insights that will help researchers develop new immunotherapies for these cancers.”