Development and validation of a nomogram for evaluating the prognosis of immunotherapy plus antiangiogenic therapy in non-small cell lung cancer

A novel model for predicting prognosis and response to immunotherapy in nasopharyngeal carcinoma patients

Blood-based biomarkers of immune checkpoint inhibitors (ICIs) response in patients with nasopharyngeal carcinoma (NPC) are lacking, so it is necessary to identify biomarkers to select NPC patients who will benefit most or least from ICIs. The absolute values of lymphocyte subpopulations, biochemical indexes, and blood routine tests were determined before ICIs-based treatments in the training cohort (n = 130). Then, the least absolute shrinkage and selection operator (Lasso) Cox regression analysis was developed to construct a prediction model. The performances of the prediction model were compared to TNM stage, treatment, and Epstein-Barr virus (EBV) DNA using the concordance index (C-index). Progression-free survival (PFS) was estimated by Kaplan-Meier (K-M) survival curve. Other 63 patients were used for validation cohort. The novel model composed of histologic subtypes, CD19+ B cells, natural killer (NK) cells, regulatory T cells, red blood cells (RBC), AST/ALT ratio (SLR), apolipoprotein B (Apo B), and lactic dehydrogenase (LDH). The C-index of this model was 0.784 in the training cohort and 0.735 in the validation cohort. K-M survival curve showed patients with high-risk scores had shorter PFS compared to the low-risk groups. For predicting immune therapy responses, the receiver operating characteristic (ROC), decision curve analysis (DCA), net reclassifcation improvement index (NRI) and integrated discrimination improvement index (IDI) of this model showed better predictive ability compared to EBV DNA. In this study, we constructed a novel model for prognostic prediction and immunotherapeutic response prediction in NPC patients, which may provide clinical assistance in selecting those patients who are likely to gain long-lasting clinical benefits to anti-PD-1 therapy.

A nomogram model for predicting the risk of checkpoint inhibitor-related pneumonitis for patients with advanced non-small-cell lung cancer

OBJECTIVE

Immunotherapy extensively treats advanced non-small-cell lung cancer (NSCLC). Although immunotherapy is generally better tolerated than chemotherapy, it can cause multiple immune-related adverse events (irAEs) involving multiple organs. Checkpoint inhibitor-related pneumonitis (CIP) is a relatively uncommon irAE that, in severe cases, can be fatal. Potential risk factors for the occurrence of CIP are currently poorly understood. This study sought to develop a novel scoring system for predicting the risk of CIP based on a nomogram model.

METHODS

We retrospectively collected advanced NSCLC patients who received immunotherapy at our institution between January 1, 2018, and December 30, 2021. All patients who met the criteria were randomly divided into the training set and testing set (in a ratio of 7:3), and cases fulfilling the CIP diagnostic criteria were screened. The patients' baseline clinical characteristics, laboratory tests, imaging, and treatment information were extracted from the electronic medical records. The risk factors associated with the occurrence of CIP were identified based on the results of logistic regression analysis on the training set, and a nomogram prediction model was developed. The discrimination and prediction accuracy of the model was evaluated using the receiver operating characteristic (ROC) curve, the concordance index (C-index), and the calibration curve. Decision curve analysis (DCA) was used to evaluate the clinical applicability of the model.

RESULTS

The training set comprised 526 (CIP: 42 cases), and the testing set comprised 226 (CIP: 18 cases) patients, respectively. In the training set, the final multivariate regression analysis revealed that age (p = 0.014; odds ratio [OR] = 1.056; 95% Confidence Interval [CI] =1.011-1.102), Eastern Cooperative Oncology Group performance status (p = 0.002; OR = 6.170; 95% CI = 1.943-19.590), history of prior radiotherapy (p < 0.001; OR = 4.005; 95% CI = 1.920-8.355), baseline white blood cell count (WBC) (p < 0.001; OR = 1.604; 95% CI = 1.250-2.059), and baseline absolute lymphocyte count (ALC) (p = 0.034; OR = 0.288; 95% CI = 0.091-0.909) were identified as independent risk factors for the occurrence of CIP. A prediction nomogram model was developed based on these five parameters. The area under the ROC curve and C-index of the prediction model in the training set and testing set were 0.787 (95% CI: 0.716-0.857) and 0.874 (95% CI: 0.792-0.957), respectively. The calibration curves are in good agreement. The DCA curves indicate that the model has good clinical utility.

CONCLUSION

We developed a nomogram model that proved to be a good assistant tool for predicting the risk of CIP in advanced NSCLC. This model has the potential power to help clinicians in making treatment decisions.