Using Artificial Intelligence to Predict Which People with Lung Cancer Will Respond to Immunotherapy (Medicine)

NCI Awards $3 Million Grant to Perlmutter Cancer Center & Case Western Reserve University

Recent advances in immunotherapy have benefited people with locally advanced or metastatic non-small cell lung cancer, but unfortunately not all patients have a favorable response to these treatments. The complexity and dynamic nature of the tumor interactions with the immune system make it challenging to develop tests to develop predictive tests (biomarkers) for immunotherapy.

The National Cancer Institute (NCI) has awarded a 5-year, $3 million grant to researchers at NYU Langone Health’s Perlmutter Cancer Center and Case Western Reserve University in Cleveland to develop and apply artificial intelligence (AI) tools for predicting which people with lung cancer will respond to immunotherapy.

Vamsidhar Velcheti, MD, director of the Thoracic Medical Oncology Program at Perlmutter Cancer Center, in collaboration with Anant Madabhushi, PhD, director of Case Western Reserve’s Center for Computational Imaging and Personalized Medicine, developed new technologies to evaluate tumor response to immunotherapy. Using advanced computer image analysis tools and AI-based algorithms, they could identify signatures, or biomarkers, on CT scan images that could predict which patients would respond to immunotherapy. The NCI grant awarded to the team will allow further development of these tools and help with clinical translation of this research.

“One of the advantages of our approach and the tools that we have developed is that we can use routinely acquired contrast enhanced CT scans, which are used commonly in lung cancer,” says Dr. Velcheti, who is also a member of the Lung Cancer Center and an associate professor in the Department of Medicine at NYU Langone. “These tools can be used to predict response to treatment and longitudinally follow a patient’s progress on treatment.”

The signatures the researchers identified can quantitatively assess the tortuosity (twistedness) of blood vessels within and surrounding the tumors. Dr. Velcheti and his colleagues found that increased vessel tortuosity around a tumor is a strong predictor of response to immunotherapy. Patients who have tumors with increased vessel tortuosity tend to have poor response to immunotherapy possibly because of impaired immune cell trafficking into the tumor.

“Some lung tumors tend to have a lot of twists and turns in the blood vessels due to secretion of VEGF (vascular endothelial growth factor, a protein that causes an increase in the number of blood vessels, fueling tumor growth). These tumors do not respond well to treatment with immunotherapy,” Dr. Velcheti says. “Using the new radiomic biomarker that measures vessel tortuosity, we can identify patients who could potentially benefit from combining immunotherapy with drugs that inhibit VEGF.”

With the NCI grant, Dr. Velcheti and colleagues at Perlmutter Cancer Center plan to develop a clinical trial to test the effect of immunotherapy combined with drugs that inhibit the VEGF protein, which would enable immune cells to gain access to the tumor.

“The NCI funding is a critical step to help translate the exciting science from the lab to the clinic,” Dr. Velcheti says. “This grant will help us continue our multidisciplinary and multi-institutional collaborative research to bring novel and efficient diagnostic tools to help patients with lung cancer.”

Featured image credit: gettyimages


Provided by NYU Langone

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