P3.13C.02 A Plasma Proteomics-Based Model for Clinical Benefit Prediction in Small Cell Lung Cancer Patients Receiving Immunotherapy
Gandara, David Carbone, David Dicker, Adam Christopoulos, Petros Puzanov, Igor Farrugia, David Brown, Sean Moskovitz, Mor Bar, Jair Hassani, Adam
Gandara, David
Carbone, David
Dicker, Adam
Christopoulos, Petros
Puzanov, Igor
Farrugia, David
Brown, Sean
Moskovitz, Mor
Bar, Jair
Hassani, Adam
Glos Author
Date
2024-10-16
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Subject
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Conference Abstract
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Abstract
Small cell lung cancer (SCLC) is an aggressive disease with limited treatment options. Immune checkpoint inhibitor (ICI) therapy with concurrent chemotherapy is the preferred first-line treatment for patients with extensive-stage SCLC. However, the addition of ICIs to chemotherapy only modestly improves clinical outcomes while posing a risk of ICI-related toxicities. Thus, identifying patients likely to benefit from ICIs is critical for optimizing treatment decisions. Here, we describe a test derived from a novel computational model that analyzes pretreatment plasma proteomic profiles to predict clinical outcomes in patients with SCLC receiving ICI-based therapies.
Citation
Gandara, D. R., Carbone, D. P., Dicker, A. P., Christopoulos, P., Puzanov, I., Jain, P., ... & Schneider, M. A. (2024). P3. 13C. 02 A Plasma Proteomics-Based Model for Clinical Benefit Prediction in Small Cell Lung Cancer Patients Receiving Immunotherapy. Journal of Thoracic Oncology, 19(10), S354-S355. 10.1016/j.jtho.2024.09.638