Researchers at The Tisch Cancer Institute at Mount Sinai, in collaboration with Memorial Sloan Kettering Cancer Center (MSK), have developed an exciting new tool that could change the way cancer patients are treated. The tool, called SCORPIO, uses artificial intelligence (AI) to predict how well cancer patients will respond to a type of treatment called immune checkpoint inhibitors (ICIs).
Not only does SCORPIO rely on widely available and affordable routine blood tests, but it also consistently outperforms existing Federal Drug Administration-approved biomarkers, such as tumor mutational burden (TMB) and PD-L1 immunostaining, in predicting patient responses to immune checkpoint inhibitors. This sets a new benchmark for precision oncology tools.
Diego Chowell, PhD, Assistant Professor of Immunology and Immunotherapy, Oncological Sciences, and Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, led the study. It was titled "Prediction of checkpoint inhibitor immunotherapy efficacy for cancer using routine blood tests and clinical data" and published today in the journal Nature Medicine.
"Immune checkpoint inhibitors are a promising cancer treatment, but they don't work for everyone," Dr. Chowell said. "Right now, doctors use expensive tests, like genetic or immune system analysis, to try to predict which patients will benefit. These tests can be costly, time-consuming, inaccurate and not always available in every hospital. SCORPIO changes that by using routine blood tests that doctors already use to monitor their patients. This makes predicting treatment success faster, simpler, more accurate and more affordable."
Immune checkpoint inhibitor drugs have gained popularity in oncology because of their ability to boost a person's immune response against cancer cells. The 2018 Nobel Prize in Physiology or Medicine was awarded to James P. Allison, PhD, and Tasuku Honjo, MD, PhD, for their research that led to the clinical development of ICIs, which have dramatically improved outcomes for many people with cancer. In the United States alone, spending on ICIs increased from $2.8 million to $4.1 billion and utilization increased from 94 to 462,049 prescriptions between 2011 and 2021, according to the Centers for Medicare and Medicaid Services.
The SCORPIO model, which has been validated using data from nearly 10,000 patients across 21 cancer types, is the first of its kind to offer a machine learning-based prediction tool that can predict immune checkpoint inhibitor response using routine clinical data such as complete blood counts and metabolic profiles. In head-to-head comparisons, SCORPIO demonstrated superior predictive power compared to Food and Drug Administration-approved biomarkers, providing a more accurate and accessible solution for oncologists worldwide.
"We are excited about the potential of this technology to democratize access to personalized cancer treatment, making cancer care more efficient, affordable, and equitable for patients," Dr. Chowell said. "SCORPIO's simplicity and affordability make it a game changer in oncology. By using readily available clinical data, we can ensure that more patients, regardless of geographic or financial barriers, have access to precision cancer care. This tool could not only improve patient outcomes but also help minimize health care costs by eliminating the need for unnecessary treatments."
"The next steps for this research are to collaborate with hospitals and cancer centers to prospectively validate the use of the model in various clinical environments and gather feedback to optimize the tool," said co-senior author Luc Morris, MD, at MSK.
The team also aims to scale SCORPIO for global use, making it accessible in resource-limited settings to promote equitable access to personalized cancer care.
"Lastly, continuous algorithm improvements will enhance SCORPIO's accuracy and predictive power, potentially extending its applications to other cancer treatments," Dr. Chowell said. "Collectively, these steps could help establish SCORPIO as a vital tool in personalized oncology, enhancing patient outcomes and health care efficiency worldwide."
This study included contributions from Mount Sinai researchers Seong-Keun Yoo, PhD; Byuri Angela Cho, PhD, and Bailey G. Fitzgerald, MD, Thomas Marron, MD, PhD, and Robert Samstein MD, PhD; and Conall Fitzgerald, a research fellow at MSK. It was a collaboration between Mount Sinai Health System and Memorial Sloan Kettering Cancer Center. Seed funding from Mount Sinai supported the study.
