Dynamic serum biomarkers to predict the efficacy of PD-1 in patients with nasopharyngeal carcinoma

Abstract

Background There is a lack of effective treatments for recurrent or metastatic nasopharyngeal carcinoma (RM-NPC). Furthermore, the response rate of NPC patients to programmed death 1 (PD-1) inhibitors is approximately 20% to 30%. Thus, we aimed to explore reliable and minimally invasive prognostic indicators to predict the efficacy of PD-1 inhibitors combination therapy in RM-NPC. Methods The serum markers of 160 RM-NPC patients were measured before and three weeks after the first anti-PD-1 treatment. The least absolute shrinkage and selection operator (LASSO) logistic regression was carried out to select dynamic serum indicators and construct a prediction model. Furthermore, we carried out univariate, multivariate, nomogram and survival analyses to identify independent prognostic factors that were associated with 1-year progression-free survival (PFS). Results Based on two markers that were screened by Lasso logistic regression, we constructed a risk score prediction model for the prediction of anti-PD-1 efficacy at 8–12 weeks with an AUC of 0.737 in the training cohort and 0.723 in the validation cohort. Risk score and metastases were included in the nomogram, and the Kaplan–Meier survival curves demonstrated that the high-risk group has shorter PFS compared to the low-risk group. The concordance index (C-index) of the nomogram for PFS is higher than that of the TNM stage in the training and validation cohort. Conclusion We proposed a strategy to monitor dynamic changes in the biochemistry markers and emphasized their importance as potential prognostic biomarkers for the treatment of advanced NPC treated with PD-1 inhibitors. Our risk score prediction model was based on the dynamic change of LDH and AST/ALT, which has predictive and prognostic value for NPC patients who were treated with PD-1 inhibitors.

Vitamin D Status Is Associated With Immune Checkpoint Inhibitor Efficacy and Immune-related Adverse Event Severity in Lung Cancer Patients: A Prospective Cohort Study

“…Possible predictive factors were included in univariable and multivariable analyses for ICI efficacy and irAE occurrence to reduce confounding bias: demographic factors (sex, age, body mass index, geographic region, and history of heavy smoking); cancer-related factors (TNM stage, pathologic type, pretreatment Eastern Cooperative Oncology Group Performance Status, driver gene mutation, prior chemotherapy/targeted/radiation therapy, concomitant chemotherapy/vascular endothelial growth factor receptor inhibitors); clinical factors (history of infection in the previous 6 months, concomitant autoimmune disease/seasonal allergies/metabolic disease/pulmonary diseases, concomitant NSAID/metformin/statin/antibiotic use); and hematological and biochemical factors (absolute lymphocyte count, absolute eosinophil count, platelet-to-lymphocyte ratio, neutrophil-tolymphocyte ratio, baseline 25(OH)D level, serum albumin level). 13,18,19 Missing data were omitted in the univariable analysis, while there were no missing data in the multivariable analysis. Variables with P-values <0.10 in univariable analyses were further analyzed in a multivariable logistic regression model by forward stepwise selection.…”

Variation of Peripheral Blood-based Biomarkers for Response of Anti-PD-1 Immunotherapy in Lung Cancer

“…[45] Therefore, its high expression suggests that the tumor is growing rapidly. Although the predictive role of in ammatory markers has been reported in many literature, [34,35,[46][47][48] unfortunately, there is no perfect and comprehensive mechanistic explanation.…”

“…[28][29][30][31] In addition, various reports have demonstrated the use of cytokines, such as IL-6, IL-8, IFN-γ, CRP, and LDH, as either predictive or prognostic factors for immunotherapy responses. [32][33][34][35] In this work, we conducted a retrospective study involving 293 patients with lung cancer under anti-PD-1 immunotherapy in order to explore the diagnostic value of dynamic biomarkers in peripheral blood. Multiple machine-learning models were compared and used to predict the therapeutic responses through blood cell analysis, blood biochemical detection, immunoassay, and peripheral immune cell subsets.…”

Correction to: Dynamic serum biomarkers to predict the efficacy of PD-1 in patients with nasopharyngeal carcinoma

“…1186/ s12935-021-02217-y. *Correspondence: chenshl@sysucc.org.cn; sunp@sysucc.org.cn; chenhao@sysucc.org.cn 1…”