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. 2024 Mar 13;12(3):e008638. doi: 10.1136/jitc-2023-008638

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© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See http://creativecommons.org/licenses/by-nc/4.0/.

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(A) Comparison of tumor mutational burden (TMB; non-synonymous mutations) between responder and non-responder groups. (B) Identification of an optimal TMB cut-off for distinguishing responders from non-responders to nivolumab treatment. (C) Overall survival of patients based on the optimal TMB cut-off. (D) Progression-free survival of patients based on the optimal TMB cut-off. (E) Comparison of tumor volume changes based on the optimal TMB cut-off. (F) Validation of the optimal TMB cut-off using the MSK-IMPACT cohort who received immune checkpoint blockades. (G) Identification of molecular and clinical correlates that are significantly associated with progression-free survival (y-axis) and the best objective response (x-axis) in response to nivolumab treatment. (H) Feature importance scores of each molecular and clinical feature against nivolumab treatment using a gradient-boosting machine algorithm. The model adopted a bootstrapping strategy 100 times to obtain a robust evaluation of the predictive features.