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. 2022 Jun 4;72(1):23–37. doi: 10.1007/s00262-022-03221-5

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© The Author(s) 2022

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 6 — A Patients were stratified into response groups based on RECIST criteria (PR, SD, and PD), duration of OS, and duration of PFS. “Long-term survival with no durable clinical benefit (NDB_LS)” was defined as patients who achieved NDB with ICI therapy (OS > 1 year) (n = 37). An additional cohort of patients who achieved short-term survival (OS < 1 year) after ICI treatment with early tumor progression (PFS < 6 months) was considered separately (n = 16). B Pie charts demonstrating the proportions of NDB_LS patients. C Waterfall plot showing the correlation of BICS with clinical status (NDB_LS, NDB_SS, and DCB). BICS: peripheral-blood-immune-cell-based signature; DCB: durable clinical outcomes; NDB_LS: long-term survival with no durable clinical benefit; NDB_SS: short-term survival with no durable clinical benefit; NDB: no durable benefit; PD: progressive disease; PR: partial response; SD: stable disease; RCC: renal cell carcinoma; ESCC: esophageal squamous cell carcinoma; NSCLC: non-small cell lung cancer; HCC: hepatocellular carcinoma. D Bar plot showing the AUC of each model for classifying NDB versus NDB_LS& DCB in both the training and validation cohorts. E The accuracy and kappa generated by bootstrapping for classifying NDB versus NDB_LS& DCB using the SVM-RFE, RFB, elastic net, and BICS models. BICS: peripheral-blood-immune-cell-based signature; SVM-RFE: support vector machine-recursive and feature elimination; RFB: random forest and Boruta