New biomarkers exploration and nomogram construction of prognostic and immune-related adverse events of advanced non-small cell lung cancer patients receiving immune checkpoint inhibitors

Abstract

Background: Immune checkpoint inhibitors (ICIs) are regarded as the most promising treatment for advanced-stage non-small cell lung cancer (aNSCLC). Unfortunately, there has been no unified accuracy biomarkers and systematic model specifically identified for prognostic and severe immune-related adverse events (irAEs). Our goal was to discover new biomarkers and develop a publicly accessible method of identifying patients who may maximize benefit from ICIs.

Methods: This retrospective study enrolled 138 aNSCLC patients receiving ICIs treatment. Progression-free survival (PFS) and severe irAEs were end-points. Data of demographic features, severe irAEs, and peripheral blood inflammatory-nutritional and immune indices before and after 1 or 2 cycles of ICIs were collected. Independent factors were selected by least absolute shrinkage and selection operator (LASSO) combined with multivariate analysis, and incorporated into nomogram construction. Internal validation was performed by applying area under curve (AUC), calibration plots, and decision curve.

Results: Three nomograms with great predictive accuracy and discriminatory power were constructed in this study. Among them, two nomograms based on combined inflammatory-nutritional biomarkers were constructed for PFS (1 year-PFS and 2 year-PFS) and severe irAEs respectively, and one nomogram was constructed for 1 year-PFS based on immune indices. ESCLL nomogram (based on ECOG PS, preSII, changeCAR, changeLYM and postLDH) was constructed to assess PFS (1-, 2-year-AUC = 0.893 [95% CI 0.837-0.950], 0.828 [95% CI 0.721-0.935]). AdNLA nomogram (based on age, change-dNLR, changeLMR and postALI) was constructed to predict the risk of severe irAEs (AUC = 0.762 [95% CI 0.670-0.854]). NKT-B nomogram (based on change-CD3+CD56+CD16+NKT-like cells and change-B cells) was constructed to assess PFS (1-year-AUC = 0.872 [95% CI 0.764-0.965]). Although immune indices could not be modeled for severe irAEs prediction due to limited data, we were the first to find CD3+CD56+CD16+NKT-like cells were not only correlated with PFS but also associated with severe irAEs, which have not been reported in the study of aNSCLC-ICIs. Furthermore, our study also discovered higher change-CD4+/CD8+ ratio was significantly associated with severe irAEs.

Conclusions: These three new nomograms proceeded from non-invasive and straightforward peripheral blood data may be useful for decisions-making. CD3+CD56+CD16+NKT-like cells were first discovered to be an important biomarker for treatment and severe irAEs, and play a vital role in distinguishing the therapy response and serious toxicity of ICIs.

Keywords: Adverse events; Biomarkers; Immune; Lung cancer; Nomogram.

Fig. 1

Flow chart of patient enrollment

Fig. 2

Factors selection and nomogram construction based on peripheral-blood inflammatory-nutritional indices of PFS. a The LASSO coefficient profiles of candidate variables; b the optimal tuning parameter lambda λ in LASSO analysis selected with tenfold cross-validation. Dotted vertical line is set at the nonzero coefficients selected via tenfold cross-validation. The first vertical line equals the minimum error. The second vertical line shows cross-validated error within one standard error of the minimum; c forest plot of multivariate analysis of PFS. d “ESCLL” nomogram (based on ECOG PS, preSII, changeCAR, changeLYM and postLDH) was established to assess 1-year and 2-year PFS; Each factor corresponds to a specific point by drawing a vertical line from that variable to the points axis. After sum of the scores for each variable located on the Total Points axis. Finally, the sum represents the probability of 1-, 2-year survival by drawing straight down to the survival axis. (For example, an aNSCLC patient with changeLYM ≥ − 0.20, preSII < 625, postLDH ≥ 228, ECOG PS < 2 and changeCAR ≥ 0.38, the total score will be given by 249, corresponding to 1- and 2-year risks of progression of 0.645 and 0.868, respectively. The patient will accordingly have approximately 35.5% and 13.2% survival probabilities at 1 and 2 years, respectively) *P < 0.05; **P < 0.01; ***P < 0.001. e The ROC curve of the nomogram for 1-year, 2-year PFS; f calibration curves of the nomogram for 1-year, 2-year PFS; g decision curve analysis of the nomogram for 1-year, 2-year PFS; h Kaplan–Meier curves depicting PFS according to risk levels (cutpoint value: 1.37). Significant difference in survival of patients was observed between high and low risk score. ECOG PS, Eastern Cooperative Oncology Group Performance Status scores; preSII, pre-treatment systemic immune inflammation index; postLDH, post-treatment lactate dehydrogenase; changeLYM, change of absolute lymphocyte count; changeCAR, change of C-reactive protein-albumin ratio; ROC, receiver operator characteristic

Fig. 3

Factors selection and nomogram construction based on peripheral-blood inflammatory-nutritional indices of severe irAEs. a The LASSO coefficient profiles of the candidate variables; b the optimal tuning parameter (lambda, λ) in the LASSO analysis selected with tenfold cross-validation via minimum criteria; c forest plot of multivariate analysis independent prognostic analysis of severe irAEs; d the “AdNLA” nomogram (based on age, change-dNLR, changeLMR and postALI) were constructed for severe irAEs prediction of aNSCLC patients receiving ICIs; e the ROC curve of the nomogram for severe irAEs; f calibration curves of the nomogram for severe irAEs; g decision curves analysis of the nomogram for severe irAEs. irAEs immune-related adverse events, change-dNLR change of derived-neutrophil–lymphocyte ratio, changeLMR change of lymphocyte-to-monocyte ratio, postALI post-treatment advanced lung cancer inflammation index

Fig. 4

Factors selection and nomogram construction based on peripheral blood immune indices of PFS. a The LASSO coefficient profiles of the candidate variables; b the optimal tuning λ in the LASSO analysis selected with tenfold cross-validation via minimum criteria; c forest plot of multivariate analysis independent prognostic analysis of PFS. d “NKT-B” nomogram (based on change-CD3+CD56+CD16+NKT-like cells and change-B cells) was established to assess 1-year PFS of aNSCLC patients receiving ICIs; e the ROC curve of the nomogram for 1-year PFS; f calibration curves of the nomogram for 1-year PFS; g decision curve analysis of the nomogram for 1-year PFS; h Kaplan–Meier curves depicting PFS according to risk levels (cut-point value: − 4.1868)

Fig. 5

Factors selection and correlation analysis based on peripheral-blood immune indices of severe irAEs. a The LASSO coefficient profiles of the candidate variables; b the optimal tuning λ in the LASSO analysis selected with tenfold cross-validation via minimum criteria; c the development of severe irAEs corresponds to change-CD4+ T cells, change-CD4+/CD8+ ratio and change-CD3+CD56+CD16+NKT-like cells; d the development of severe irAEs corresponds to age and hypertension