Decreased monocyte-to-lymphocyte ratio was associated with satisfied outcomes of first-line PD-1 inhibitors plus chemotherapy in stage IIIB-IV non-small cell lung cancer

Real‑world evidence of advanced non‑small cell lung carcinoma treated with an immune checkpoint inhibitor plus chemotherapy

Immunotherapy is an effective treatment strategy for patients with advanced non-small cell lung cancer (NSCLC). Although clinical trials on immunotherapy have provided promising results, real-world research in clinical practice is needed to assess the effectiveness and safety of immunotherapy. The present study aimed to characterize real-world outcomes in patients with advanced NSCLC treated with immune checkpoint inhibitor (ICI)-based regimens. The medical records of patients with advanced NSCLC, who were treated with programmed cell death protein-1 (PD-1)/programmed cell death 1 ligand 1 (PD-L1) inhibitors, were reviewed for data collection. The primary objectives were to evaluate progression-free survival (PFS) and overall survival (OS). Therefore, multiple Cox regression models were used to investigate the predictive factors for survival outcomes. Furthermore, survival curves for PFS and OS were created using Kaplan-Meier estimates and compared using the log-rank test. The present study included a total of 133 patients with advanced NSCLC who received therapy with ICIs between January 1, 2019 and December 31, 2022. The final follow-up date was August 24, 2023. The median PFS and OS times were 9.8 and 27.2 months, respectively. Univariate Cox regression analysis demonstrated that sex, clinical stage, PD-L1 status, previous systemic therapy, and brain and liver metastases were associated with PFS, while Eastern Cooperative Oncology Group (ECOG) status, clinical stage, PD-L1 status and brain metastasis were associated with OS. Furthermore, multivariate Cox regression analysis demonstrated that a PD-L1 tumor proportion score (TPS) of ≥50% was an indicator of favorable PFS and OS. An ECOG performance status score of ≥1 was also associated with poor OS but not with PFS. Furthermore, brain metastasis was an indicator for poor PFS and OS, while liver metastasis was only associated with a poor PFS. Finally, the results of the present study demonstrated that PD-L1 status was an independent predictor for PFS and OS in patients with advanced NSCLC, especially adenocarcinoma, who were treated with ICIs plus chemotherapy. The results also suggested that patients with a PD-L1 TPS of ≥50% could benefit when the aforementioned regimens were administrated as a first-line or later-line therapy.

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.

Clinical characteristics and novel strategies of immune checkpoint inhibitor rechallenge therapy for non-small cell lung cancer: a comprehensive review

Treatment of non-small-cell lung cancer (NSCLC) has entered the immunotherapy era, marked by significant survival improvements due to the use of immune checkpoint inhibitors (ICIs). However, owing to factors, such as disease progression, long-term use, and side effects, some patients discontinue immunotherapy, resulting in limited subsequent treatment option and a negative impact on their survival and quality of life. We have collected relevant data which reveal that ICI rechallenge may be an effective clinical strategy. However, many factors affect the efficacy of rechallenge, including patient characteristics, initial treatment drugs, treatment duration, efficacy, toxicity, and side effects. Additionally, the side effects of rechallenge and mechanisms of reversing drug resistance play crucial roles. Identifying suitable candidates, optimizing treatment plans and duration, enhancing treatment efficacy, and minimizing toxicity and adverse effects in rechallenges are pressing clinical needs. Addressing these issues can provide guidance for the clinical use of immunotherapy rechallenges to better serve patients. This review focuses on the clinical considerations and strategies for immune therapy rechallenges in NSCLC.

Clinicopathological characteristics correlated with programmed cell death-ligand 1 expression in advanced lung adenocarcinoma

Background

Recent studies have shown that immune checkpoint inhibitors (ICIs) targeting programmed cell death-ligand 1 (PD-L1) have potential benefits in patients with non-small cell lung cancer (NSCLC) subgroups, while the clinicopathological characteristics associated with PD-L1 expression have not been well established. The purpose of this study was to detect the expression level of PD-L1 in tumor tissues of patients with advanced lung adenocarcinoma (ADC) and analyze its possible relationship with clinicopathological characteristics, so as to identify the predictors of PD-L1 expression.

Methods

This retrospective study was conducted by analyzing the clinicopathological and imaging characteristics of hospitalized advanced lung ADC patients with PD-L1 available data and admitted to the respiratory department of our hospital. The expression level of PD-L1 in fresh-frozen tumor tissue samples of 136 advanced ADC patients was analyzed by immunohistochemistry. The patients were divided into positive and negative groups based on a cut-off of 1% PD-L1 expression level. Subsequently, the significant correlation between PD-L1 levels and clinicopathological features were evaluated. The predictive performance of clinicopathological characteristics on PD-L1 expression was evaluated and the optimal cut-off values were identified by plotting the receiver operating characteristic (ROC) curve.

Results

The expression level of PD-L1 was related to sex, clinical stage, serum carcinoembryonic antigen (CEA), neuron specific enolase (NSE), white blood cell (WBC), and tumor (T) and metastasis (M) stage. Multivariate logistic regression analysis showed the CEA, NSE, T stage, and WBC were independent predictors of PD-L1 positive expression in lung ADC patients. The ROC curve suggested the model combining CEA with NSE [area under the curve (AUC) =0.815] could better predict the expression levels of PD-L1. The optimal cut-off values for identifying advanced lung ADC patients with PD-L1 positive were CEA ≤13.38 ng/mL and NSE ≤42.35 ng/mL, with sensitivity and specificity of 85.4% and 55.6%, and 92.7% and 32.1%, respectively.

Conclusions

Some commonly used clinicopathological features are related to the histological expression of PD-L1. The serum CEA, NSE, T stage, and WBC values can be used as indicators to predict the expression level of PD-L1 in advanced lung ADC, and are used as predictors to evaluate the efficacy of ICIs before treatment.

Impact of a novel immune and nutritional score on prognosis in patients with upper urinary tract urothelial carcinoma following radical nephroureterectomy

BACKGROUND

This study aimed to evaluate the clinical significance of a novel immune and nutritional score combining prognostic values of the controlling nutritional status (CONUT) score and prognostic immune and nutritional index (PINI) on long-term outcomes in patients with upper tract urothelial carcinoma (UTUC) after radical nephroureterectomy (RNU).

METHODS

This study analyzed 437 consecutive patients with UTUC treated by RNU. Restricted cubic splines were used to visualize the relation of PINI with Survival in patients with UTUC. The PINI was stratified into low- (1) and high-PINI (0) categories. The CONUT score was divided into three groups: Normal (1), Light (2), and Moderate/severe (3). Subsequently, patients were grouped according to CONUT-PINI score (CPS) (CPS group 1; CPS group 2; CPS group 3; and CPS group 4). Survival curves were plotted using the Kaplan-Meier method and log-rank test. The Cox proportional hazards regression model was used to determine the risk factors associated with overall Survival (OS) and cancer-specific Survival (CSS). By comprising independent prognostic factors, a predictive nomogram was constructed.

RESULTS

PINI and CONUT score were identified as independent prognostic factors for OS and CSS. Kaplan-Meier survival analysis showed that the high CPS group was associated with worse OS and CSS than the low CPS group. Multivariate Cox regression and competing risk analyses showed that CPS, LVI, T stage, margin, and pN were independent factors associated with OS and CSS. Based on these five significant factors, we constructed a prognostic model for predicting clinical outcomes. The receiver operating characteristic curve indicated that the model had excellent predictive abilities for survival. The C-index of this model for OS and CSS were 0.773, and 0.789, respectively. The nomogram for OS and CSS showed good discrimination and calibration. Decision curve analysis (DCA) showed that this nomogram has a higher net benefit.

CONCLUSION

The CPS combined the prognostic capacity of PINI and CONUT score and was able to predict patient outcomes in our cohort of UTUC patients. We have developed a nomogram to facilitate the clinical use of the CPS and provide accurate estimates of survival for individuals.

Neutrophil to Lymphocyte ratio as a predictor for immune-related adverse events in cancer patients treated with immune checkpoint inhibitors: a systematic review and meta-analysis

Background

The use of immune checkpoint inhibitors (ICIs) in cancer treatment has led to an increase in immune-related adverse events (irAEs), which can cause treatment discontinuation and even fatal reactions. The purpose of this study was to evaluate the usefulness of the peripheral biomarker neutrophil to lymphocyte ratio (NLR) in predicting irAEs.

Methods

A systematic search of databases was conducted to identify studies on the predictive value of NLR for irAEs. The standardized mean difference (SMD) was used to compare continuous NLR, while crude odds ratios (ORs) were calculated for categorized NLR if adjusted ORs and 95% confidence intervals (CIs) were not provided in the original study.

Results

The meta-analysis included 47 studies with a total of 11,491 cancer patients treated with ICIs. The baseline continuous NLR was significantly lower in patients with irAEs compared to those without (SMD=-1.55, 95%CI=-2.64 to -0.46, P=0.006). Similarly, categorized NLR showed that lower baseline NLR was associated with increased irAEs (OR=0.55, 95%CI=0.41-0.73, P<0.001). Subgroup analysis revealed that the OR for predicting irAEs with NLR cut-off values of 3 and 5 was 0.4 and 0.59, respectively. Interestingly, increased baseline NLR was associated with a higher incidence of immune-related liver injury (OR=2.44, 95%CI=1.23-4.84, I2 = 0%, P=0.010).

Conclusion

Our study suggests that lower baseline NLR is associated with a higher risk of overall irAEs. However, further studies are needed to determine the best cut-off value and explore the efficacy of NLR in predicting specific types of irAEs.

Pan-Immune-Inflammatory Value in Patients with Non-Small-Cell Lung Cancer Undergoing Neoadjuvant Immunochemotherapy

Background

We aimed to investigate the predictive value of a systematic serum inflammation index, pan-immune-inflammatory value (PIV), in pathological complete response (pCR) of patients treated with neoadjuvant immunotherapy to further promote ideal patients' selection.

Methods

The clinicopathological and baseline laboratory information of 128 NSCLC patients receiving neoadjuvant immunochemotherapy between October 2019 and April 2022 were retrospectively reviewed. We performed least absolute shrinkage and selection operator (LASSO) algorithm to screen candidate serum biomarkers for predicting pCR, which further entered the multivariate logistic regression model to determine final biomarkers. Accordingly, a diagnostic model for predicting individual pCR was established. Kaplan-Meier method was utilized to estimate curves of disease-free survival (DFS), and the Log rank test was analyzed to compare DFS differences between patients with and without pCR.

Results

Patients with NSCLC heterogeneously responded to neoadjuvant immunotherapy, and those with pCR had a significant longer DFS than patients without pCR. Through LASSO and the multivariate logistic regression model, PIV was identified as a predictor for predicting pCR of patients. Subsequently, a diagnostic model integrating with PIV, differentiated degree and histological type was constructed to predict pCR, which presented a satisfactory predictive power (AUC, 0.736), significant agreement between actual and our nomogram-predicted pathological response.

Conclusion

Baseline PIV was an independent predictor of pCR for NSCLC patients receiving neoadjuvant immunochemotherapy. A significantly longer DFS was achieved in patients with pCR rather than those without pCR; thus, the PIV-based diagnostic model might serve as a practical tool to identify ideal patients for neoadjuvant immunotherapeutic guidance.