Baseline gene expression profiling determines long-term benefit to programmed cell death protein 1 axis blockade

• Published in: NPJ precision oncology Vol. 6; no. 1; p. 92
• Main Authors: Vathiotis, Ioannis A., Salichos, Leonidas, Martinez-Morilla, Sandra, Gavrielatou, Niki, Aung, Thazin Nwe, Shafi, Saba, Wong, Pok Fai, Jessel, Shlomit, Kluger, Harriet M., Syrigos, Konstantinos N., Warren, Sarah, Gerstein, Mark, Rimm, David L.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 15.12.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 692/53/2423; 692/699/67/1813/1634; Apoptosis; Cancer Research; Cell death; Gene expression; Gene Therapy; Human Genetics; Immunotherapy; Internal Medicine; Medicine; Medicine & Public Health; Melanoma; Oncology; Skin cancer
• ISSN: 2397-768X; 2397-768X
• DOI: 10.1038/s41698-022-00330-3

Treatment with immune checkpoint inhibitors has altered the course of malignant melanoma, with approximately half of the patients with advanced disease surviving for more than 5 years after diagnosis. Currently, there are no biomarker methods for predicting outcome from immunotherapy. Here, we obtained transcriptomic information from a total of 105 baseline tumor samples comprising two cohorts of patients with advanced melanoma treated with programmed cell death protein 1 (PD-1)-based immunotherapies. Gene expression profiles were correlated with progression-free survival (PFS) within consecutive clinical benefit intervals (i.e., 6, 12, 18, and 24 months). Elastic net binomial regression models with cross validation were utilized to compare the predictive value of distinct genes across time. Lasso regression was used to generate a signature predicting long-term benefit (LTB), defined as patients who remain alive and free of disease progression at 24 months post treatment initiation. We show that baseline gene expression profiles were consistently able to predict long-term immunotherapy outcomes with high accuracy. The predictive value of different genes fluctuated across consecutive clinical benefit intervals, with a distinct set of genes defining benefit at 24 months compared to earlier outcomes. A 12-gene signature was able to predict LTB following anti-PD-1 therapy with an area under the curve (AUC) equal to 0.92 and 0.74 in the training and validation set, respectively. Evaluation of LTB, via a unique signature may complement objective response classification and characterize the logistics of sustained antitumor immune responses.