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

APLNR inhibited nasopharyngeal carcinoma growth and immune escape by downregulating PD-L1

BACKGROUND

APLNR is a G protein-coupled receptor and our previous study had revealed that APLNR could inhibit nasopharyngeal carcinoma (NPC) growth and metastasis. However, the role of APLNR in regulating PD-L1 expression and immune escape in NPC is unknown.

METHODS

We analyzed the expression and correlation of APLNR and PD-L1 in NPC tissues and cells. We investigated the effect of APLNR on PD-L1 expression and the underlying mechanism in vitro and in vivo. We also evaluated the therapeutic potential of targeting APLNR in combination with PD-L1 antibody in a nude mouse xenograft model.

RESULTS

We found that APLNR was negatively correlated with PD-L1 in NPC tissues and cells. APLNR could inhibit PD-L1 expression by binding to the FERM domain of JAK1 and blocking the interaction between JAK1 and IFNGR1, thus suppressing IFN-γ-mediated activation of the JAK1/STAT1 pathway. APLNR could also inhibit NPC immune escape by enhancing IFN-γ secretion and CD8+ T-cell infiltration and reducing CD8+ T-cell apoptosis and dysfunction. Moreover, the best effect was achieved in inhibiting NPC growth in nude mice when APLNR combined with PD-L1 antibody.

CONCLUSIONS

Our study revealed a novel mechanism of APLNR regulating PD-L1 expression and immune escape in NPC and suggested that APLNR maybe a potential therapeutic target for NPC immunotherapy.

Machine learning models for predicting of PD-1 treatment efficacy in Pan-cancer patients based on routine hematologic and biochemical parameters

Immune checkpoint blockade therapy targeting the programmed death-1(PD-1) pathway has shown remarkable efficacy and durable response in patients with various cancer types. Early prediction of therapeutic efficacy is important for optimizing treatment plans and avoiding potential side effects. In this work, we developed an efficient machine learning prediction method using routine hematologic and biochemical parameters to predict the efficacy of PD-1 combination treatment in Pan-Cancer patients. A total of 431 patients with nasopharyngeal carcinoma, esophageal cancer and lung cancer who underwent PD-1 checkpoint inhibitor combination therapy were included in this study. Patients were divided into two groups: progressive disease (PD) and disease control (DC) groups. Hematologic and biochemical parameters were collected before and at the third week of PD-1 therapy. Six machine learning models were developed and trained to predict the efficacy of PD-1 combination therapy at 8-12 weeks. Analysis of 57 blood biomarkers before and after three weeks of PD-1 combination therapy through statistical analysis, heatmaps, and principal component analysis did not accurately predict treatment outcome. However, with machine learning models, both the AdaBoost classifier and GBDT demonstrated high levels of prediction efficiency, with clinically acceptable AUC values exceeding 0.7. The AdaBoost classifier exhibited the highest performance among the 6 machine learning models, with a sensitivity of 0.85 and a specificity of 0.79. Our study demonstrated the potential of machine learning to predict the efficacy of PD-1 combination therapy based on changes in hematologic and biochemical parameters.

A nomogram based on the SII3 and clinical indicators predicts survival in patients with nasopharyngeal carcinoma treated with PD-1 inhibitors

Numerous inflammatory indicators have been demonstrated to be strongly correlated with tumor prognosis. However, the association between inflammatory indicators and the prognosis of patients with nasopharyngeal carcinoma (NPC) receiving treatment with programmed death receptor-1 (PD-1) immunosuppressant monoclonal antibodies remains uncertain. Inflammatory indicators in peripheral blood were collected from 161 NPC patients at 3 weeks after initial PD-1 treatment. Through univariate and multivariate analyses, as well as nomogram and survival analyses, we aimed to identify independent prognostic factors related to 1-year progression-free survival (PFS). Subsequently, a prognostic nomogram was devised, and its predictive and discriminating abilities were assessed utilizing calibration curves and the concordance index. Our univariate and multivariate analyses indicated that age (P = .012), M stage (P < .001), and systemic immune-inflammation index (SII) during the third week following initial PD-1 treatment (SII3, P = .005) were independently correlated with the 1-year PFS of NPC patients after PD-1 treatment. Notably, we constructed a novel nomogram based on the SII3, age, and M stage. Importantly, utilizing the derived cutoff point from the nomogram, the high-risk group exhibited significantly shorter PFS than did the low-risk group (P < .001). Furthermore, the nomogram demonstrated a greater concordance index for PFS than did the tumor node metastasis stage within the entire cohort. We successfully developed a nomogram that integrates the SII3 and clinical markers to accurately predict the 1-year PFS of NPC patients receiving PD-1 inhibitor treatment.

Development and validation of a nomogram to predicting the efficacy of PD-1/PD-L1 inhibitors in patients with nasopharyngeal carcinoma

PURPOSE

With the treatment of nasopharyngeal carcinoma (NPC) by PD-1/PD-L1 inhibitors used widely in clinic, it becomes very necessary to anticipate whether patients would benefit from it. We aimed to develop a nomogram to evaluate the efficacy of anti-PD-1/PD-L1 in NPC patients.

METHODS

Totally 160 NPC patients were enrolled in the study. Patients were measured before the first PD-1/PD-L1 inhibitors treatment and after 8-12 weeks of immunotherapy by radiological examinations to estimate the effect. The least absolute shrinkage and selection operator (LASSO) logistic regression was used to screen hematological markers and establish a predictive model. The nomogram was internally validated by bootstrap resampling and externally validated. Performance of the model was evaluated using concordance index, calibration curve, decision curve analysis and receiver operation characteristic curve.

RESULTS

Patients involved were randomly split into training cohort ang validation cohort. Based on Lasso logistic regression, systemic immune-inflammation index (SII) and ALT to AST ratio (LSR) were selected to establish a predictive model. The C-index of training cohort and validating cohort was 0.745 and 0.760. The calibration curves and decision curves showed the precise predictive ability of this nomogram. The benefit of the model showed in decision curve was better than TNM stage. The area under the curve (AUC) value of training cohort and validation cohort was 0.745 and 0.878, respectively.

CONCLUSION

The predictive model helped evaluating efficacy with high accuracy in NPC patients treated with PD-1/PD-L1 inhibitors.

Biomarkers and computational models for predicting efficacy to tumor ICI immunotherapy

Numerous studies have shown that immune checkpoint inhibitor (ICI) immunotherapy has great potential as a cancer treatment, leading to significant clinical improvements in numerous cases. However, it benefits a minority of patients, underscoring the importance of discovering reliable biomarkers that can be used to screen for potential beneficiaries and ultimately reduce the risk of overtreatment. Our comprehensive review focuses on the latest advancements in predictive biomarkers for ICI therapy, particularly emphasizing those that enhance the efficacy of programmed cell death protein 1 (PD-1)/programmed cell death-ligand 1 (PD-L1) inhibitors and cytotoxic T-lymphocyte antigen-4 (CTLA-4) inhibitors immunotherapies. We explore biomarkers derived from various sources, including tumor cells, the tumor immune microenvironment (TIME), body fluids, gut microbes, and metabolites. Among them, tumor cells-derived biomarkers include tumor mutational burden (TMB) biomarker, tumor neoantigen burden (TNB) biomarker, microsatellite instability (MSI) biomarker, PD-L1 expression biomarker, mutated gene biomarkers in pathways, and epigenetic biomarkers. TIME-derived biomarkers include immune landscape of TIME biomarkers, inhibitory checkpoints biomarkers, and immune repertoire biomarkers. We also discuss various techniques used to detect and assess these biomarkers, detailing their respective datasets, strengths, weaknesses, and evaluative metrics. Furthermore, we present a comprehensive review of computer models for predicting the response to ICI therapy. The computer models include knowledge-based mechanistic models and data-based machine learning (ML) models. Among the knowledge-based mechanistic models are pharmacokinetic/pharmacodynamic (PK/PD) models, partial differential equation (PDE) models, signal networks-based models, quantitative systems pharmacology (QSP) models, and agent-based models (ABMs). ML models include linear regression models, logistic regression models, support vector machine (SVM)/random forest/extra trees/k-nearest neighbors (KNN) models, artificial neural network (ANN) and deep learning models. Additionally, there are hybrid models of systems biology and ML. We summarized the details of these models, outlining the datasets they utilize, their evaluation methods/metrics, and their respective strengths and limitations. By summarizing the major advances in the research on predictive biomarkers and computer models for the therapeutic effect and clinical utility of tumor ICI, we aim to assist researchers in choosing appropriate biomarkers or computer models for research exploration and help clinicians conduct precision medicine by selecting the best biomarkers.

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.

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

Vitamin D (VitD) is potentially immunomodulatory, so here we aimed to explore the relationships between serum VitD levels, immune checkpoint inhibitor (ICI) efficacy, and immune-related adverse events (irAEs). Serum 25-hydroxyvitamin D [25(OH)D] levels were quantified before and after ICI treatment in prospectively enrolled patients with advanced lung cancers. Of 77 enrolled patients, 29 developed 42 irAEs. Baseline 25(OH)D levels of partial response (PRs) patients were significantly higher than non-PR patients (19.39±7.16 vs. 16.28±5.99 ng/mL, P =0.04). The area under the curve of 25(OH)D >15.73 ng/mL to identify PR was 0.63 (95% CI, 0.51-0.76, P =0.047), and baseline 25(OH)D levels >15.73 ng/mL (odds ratio: 2.93, 95% CI, 1.10-7.79, P =0.03) and prior targeted therapy (odds ratio: 0.30, 95% CI, 0.10-0.92, P =0.04) were independent predictors of PR as best efficacy by multivariable logistic regression. With respect to irAEs, baseline 25(OH)D levels were higher in grade 1 irAE patients than in grade 2/3/4 irAE patients (20.07±8.64 vs. 15.22±2.30 ng/mL, P =0.02). However, the area under the curve was only 0.56 (95% CI, 0.42-0.70, P =0.39) for a baseline 25(OH)D of 20.99 ng/mL for predicting irAE occurrence. There was a direct monotonic relationship and U-shaped relationship between baseline 25(OH)D levels and ICI efficacy and irAE occurrence, respectively. Overall survival was significantly different between VitD sufficient, insufficient, and deficient patients (log-rank P =0.01), which remained after adjustment in Cox proportional hazards regression models. Baseline 25(OH)D levels seem to be associated with ICI efficacy and prognosis, it might be helpful to assess the baseline VitD status, and supplementation with VitD might bring some benefit to enhance ICI efficacy and reduce moderate-severe irAEs.

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

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.