A novel prognostic model predicts overall survival in patients with nasopharyngeal carcinoma based on clinical features and blood biomarkers

Epidemiology of nasopharyngeal carcinoma: current insights and future outlook

Nasopharyngeal carcinoma (NPC) is characterised by its remarkable geographical and ethnic distribution. The interplay between genetic susceptibility, environmental exposures, and Epstein-Barr virus (EBV) infections is indicated in the development of NPC. Exposure to tobacco smoking, dietary factors, and inhalants has been associated with the risk of NPC. Genetic association studies have revealed NPC-associated susceptibility loci, including genes involved in immune responses, xenobiotic metabolism, genome maintenance, and cell cycle regulation. EBV exposure timing and strain variation might play a role in its carcinogenicity, although further investigations are required. Other factors including medical history and oral hygiene have been implicated in NPC. Prevention strategies, including primary prevention and secondary prevention through early detection, are vital in reducing mortality and morbidity of NPC. The current review discusses the global and regional distribution of NPC incidences, the risk factors associated with NPC, and the public health implications of these insights. Future investigations should consider international, large-scale prospective studies to elucidate the mechanisms underlying NPC pathogenesis and develop individualized interventions for NPC.

A Novel Prognostic Model Predicts Outcomes in Non-Metastatic Nasopharyngeal Carcinoma Based on Inflammation, Nutrition, and Coagulation Signature

Purpose

This study aimed to assess the prognostic and predictive value of a circulating hematological signature (CHS) and to develop a CHS-based nomogram for predicting prognosis and guiding individualized chemotherapy in non-metastatic nasopharyngeal carcinoma (NPC) patients.

Patients and Methods

NPC patients were recruited between January 2014 and December 2017 at the Jiangxi Cancer Hospital. The CHS was constructed based on a series of hematological indicators. The nomogram was developed by CHS and clinical factors.

Results

A total of 779 patients were included. Three biomarkers were selected by least absolute shrinkage and selection operator regression, including prognostic nutritional index, albumin-to-fibrinogen ratio, and prealbumin-to-fibrinogen ratio, were used to construct the CHS. The patients in the low-CHS group had better 5-year DMFS and OS than those in the high-CHS group in the training (DMFS: 85.0% vs 56.6%, p<0.001; OS: 90.3% vs 65.4%, p<0.001) and validation cohorts (DMFS: 92.3% vs 43.6%, p<0.001; OS: 92.1% vs 65.5%, p<0.001). The nomogram_CHS showed better performance than clinical stage in predicting distant metastasis (concordance index: 0.728 vs 0.646). In the low-TRS (total risk scores) group, the patients received RT alone, CCRT and IC plus CCRT had similar 5-year DMFS and OS (p>0.05). In the middle-TRS group, the patients received RT alone had worse 5-year DMFS (58.7% vs 80.8% vs 90.8%, p=0.002) and OS (75.0% vs 94.1% vs 95.0%, p=0.001) than those received CCRT or IC plus CCRT. In the high-TRS group, the patients received RT alone and CCRT had worse 5-year DMFS (18.6% vs 31.3% vs 81.5%, p<0.001) and OS (26.9% vs 53.2% vs 88.8%, p<0.001) than those received IC plus CCRT.

Conclusion

The developed nomogram_CHS had satisfactory prognostic accuracy in NPC patients and may individualize risk estimation to facilitate the identification of suitable IC candidates.

Nomogram Based on Liver Function Test Indicators for Survival Prediction in Nasopharyngeal Carcinoma Patients Receiving PD-1 Inhibitor Therapy

PURPOSE

The aim of this study was to investigate the prognostic significance of PD-1 inhibitor therapy in nasopharyngeal carcinoma (NPC) and to develop a nomogram to estimate individual risks.

METHODS

We retrospectively analyzed 162 NPC patients who were administered the PD-1 inhibitor combined with radiotherapy and chemotherapy at the Sun Yat-Sen University Cancer Center. In total, 108 NPC patients were included in the training cohort and 54 NPC patients were included in the validation cohort. Univariate and multivariate Cox survival analyses were performed to determine the prognostic factors for 1-year and 2-year progression-free survival (PFS). In addition, a nomogram model was constructed to predict the survival probability of PFS. A consistency index (C-index), a decision curve, a clinical impact curve, and a standard curve were used to measure predictive accuracy, the clinical net benefit, and the consistency of prognostic factors.

RESULTS

Univariate and multivariate analyses indicated that the metastasis stage, the levels of ALT, the AST/ALT ratio, and the LDH were independent risk factors associated with the prognosis of PD-1 inhibitor therapy. A nomogram based on these four indicators was constructed and the Kaplan-Meier survival analysis showed that patients with a higher total score have a shorter PFS. The C-index of this model was 0.732 in the training cohort and 0.847 in the validation cohort, which are higher than those for the TNM stages (training cohort: 0.617; validation cohort: 0.727; p <0.05). Decision Curve Analysis (DCA), Net Reclassification Improvement (NRI), and Integrated Discrimination Improvement (IDI) showed that our model has better prediction accuracy than TNM staging.

CONCLUSIONS

Predicting PFS in NPC patients based on liver function-related indicators before PD-1 treatment may help clinicians predict the efficacy of PD-1 treatment in these patients.

Advanced lung cancer inflammation index predicts survival outcomes of hepatocellular carcinoma patients receiving immunotherapy

Objective

We evaluate the predictive significance of the Advanced Lung Cancer Inflammation Index (ALI) in patients with advanced hepatocellular carcinoma (HCC) following therapy with immune checkpoint drugs.

Methods

In 2018-2020, 98 patients with advanced hepatocellular carcinoma who were treated with immune checkpoint inhibitors at our hospital were compiled. Using the receiver operating characteristic (ROC) curve, the appropriate cut-off point for ALI was determined. Kaplan-Meier analysis, the Cox proportional hazards model, and Nomogram plots highlighted the relationship between ALI and overall survival (OS). The model was validated using calibration plots, receiver operating characteristic curves (ROC), and decision curve analysis (DCA), which was performed on 52 patient sets by external validation.

Results

The AUC for ALI was 0.663. The best cutoff value was 36.5, with a median overall survival (OS) of 473 days for patients with ALI≤ 36.5 and 611 days for those with ALI > 36.5. Univariate analysis revealed that the presence or absence of local treatment, alpha-fetoprotein (AFP), and ALI were prognostic factors; LASSO regression analysis identified four candidate variables. Multifactorial COX analysis revealed that high ALI was an independent prognostic factor for overall survival in both groups (HR = 0.411; 95% CI: 0.244-0.651; P<0.001). In addition, the Nomogram model that included ALI was able to predict the success of immunotherapy in patients with advanced liver cancer more accurately.

Conclusion

ALI is a novel prognostic marker in immunotherapy-treated patients with advanced hepatocellular cancer.

Cytoplasmic poly(A)-binding protein 1 (PABPC1) is a prognostic biomarker to predict survival in nasopharyngeal carcinoma regardless of chemoradiotherapy

BACKGROUND

Nasopharyngeal carcinoma (NPC), especially the nonkeratinizing type, is a malignant tumor primarily occurring in southern China and Southeast Asia. Chemotherapy (CT) and combined radiotherapy (RT) is used to treat NPC. However, the mortality rate is high in recurrent and metastatic NPC. We developed a molecular marker, analyzed its correlation with clinical characteristics, and assessed the prognostic value among NPC patients with or without chemoradiotherapy.

METHODS

A total of 157 NPC patients were included in this study, with 120 undergoing treatment and 37 without treatment. EBER1/2 expression was investigated using in situ hybridization (ISH). Expression of PABPC1, Ki-67, and p53 was detected with immunohistochemistry. The correlations of EBER1/2 and the expression of the three proteins having clinical features and prognosis were evaluated.

RESULTS

The expression of PABPC1 was associated with age, recurrence, and treatment but not with gender, TNM classification, or the expression of Ki-67, p53, or EBER. High expression of PABPC1 was associated with poor overall survival (OS) and disease-free survival (DFS) and was an independent predictor depending on multivariate analysis. Comparatively, no significant correlation was observed between the expression of p53, Ki-67, and EBER and survival. In this study, 120 patients received treatments and revealed significantly better OS and DFS than the untreated 37 patients. PABPC1 high expression was an independent predictor of shorter OS in the treated (HR = 4.012 (1.238-13.522), 95% CI, p = 0.021) and the untreated groups (HR = 5.473 (1.051-28.508), 95% CI, p = 0.044). However, it was not an independent predictor of shorter DFS in either the treated or the untreated groups. No significant survival difference was observed between patients with docetaxel-based induction chemotherapy (IC) + concurrent chemoradiotherapy (CCRT) and those with paclitaxel-based IC + CCRT. However, when combined with treatment and PABPC1 expression, patients with paclitaxel-added chemoradiotherapy plus PABPC1 low expression had significantly better OS than those who underwent chemoradiotherapy (p = 0.036).

CONCLUSIONS

High expression of PABPC1 is associated with poorer OS and DFS among NPC patients. Patients with PABPC1 having low expression revealed good survival irrespective of the treatment received, indicating that PABPC1 could be a potential biomarker for triaging NPC patients.

Building Practical Risk Prediction Models for Nasopharyngeal Carcinoma Screening with Patient Graph Analysis and Machine Learning

BACKGROUND

To expand nasopharyngeal carcinoma (NPC) screening to larger populations, more practical NPC risk prediction models independent of Epstein-Barr virus (EBV) and other lab tests are necessary.

METHODS

Patient data before diagnosis of NPC were collected from hospital electronic medical records (EMR) and used to develop machine learning (ML) models for NPC risk prediction using XGBoost. NPC risk factor distributions were generated through connection delta ratio (CDR) analysis of patient graphs. By combining EMR-wide ML with patient graph analysis, the number of variables in these risk models was reduced, allowing for more practical NPC risk prediction ML models.

RESULTS

Using data collected from 1,357 patients with NPC and 1,448 patients with control, an optimal set of 100 variables (ov100) was determined for building NPC risk prediction ML models that had, the following performance metrics: 0.93-0.96 recall, 0.80-0.92 precision, and 0.83-0.94 AUC. Aided by the analysis of top CDR-ranked risk factors, the models were further refined to contain only 20 practical variables (pv20), excluding EBV. The pv20 NPC risk XGBoost model achieved 0.79 recall, 0.94 precision, 0.96 specificity, and 0.87 AUC.

CONCLUSIONS

This study demonstrated the feasibility of developing practical NPC risk prediction models using EMR-wide ML and patient graph CDR analysis, without requiring EBV data. These models could enable broader implementation of NPC risk evaluation and screening recommendations for larger populations in urban community health centers and rural clinics.

IMPACT

These more practical NPC risk models could help increase NPC screening rate and identify more patients with early-stage NPC.

The value of the Advanced Lung Cancer Inflammation Index (ALI) in assessing the prognosis of patients with hepatocellular carcinoma treated with camrelizumab: a retrospective cohort study

Background

The Advanced Lung Cancer Inflammation Index (ALI) is considered a useful prognostic biomarker for clinical outcome in patients with malignancy. However, the prognostic value of ALI in patients with advanced hepatocellular carcinoma (HCC) is unclear. In this study we assessed the prognostic value of the ALI in patients with HCC treated with camrelizumab.

Methods

This retrospective study analyzed patients with advanced hepatocellular carcinoma treated with the ICI, camrelizumab alone or in combination at Henan Cancer Hospital from January 2017 to January 2020. Sixty-five patients were finally screened for at least 2 years of follow-up according to the inclusion criteria, with no significant differences in patient baseline data. The receiver operating characteristic (ROC) curve was used to determine the optimal cut-off point for the ALI which was compared to other clinical indicators for predicting survival. A Kaplan-Meier analysis and Cox proportional analysis were conducted to examine the association between the ALI and patient prognosis.

Results

The median overall survival (OS) for the overall group of patients was 383 days, the area under the curve for ALI was 0.815 and the optimal cut-off value for predicting OS was 34.65. The median OS for patients with an ALI score ≤34.65 was 336 days and that for patients with an ALI score >34.65 was 524 days. The univariate analysis showed that the Eastern Cooperative Oncology Group (ECOG) score, aspartate aminotransferase (AST) level, and the ALI score predicted OS. The multivariate analysis showed that the ALI score was an independent prognostic factor of OS in patients with advanced HCC who had been treated with immunotherapy [hazard ratio (HR) =0.285, 95% confidence interval (CI): 0.097-0.833, P=0.022]. A nomogram that included ALI performed well relative to the prediction of prognosis after immunotherapy for patients with advanced liver cancer.

Conclusions

The ALI may be a new prognostic marker in patients with advanced HCC undergoing immunotherapy.

Development and validation of a novel prognostic model for patients with surgically resected esophageal squamous cell carcinoma

Background and objectives

Esophageal squamous cell carcinoma (ESCC) is the most common pathological type of esophageal malignancy in most regions of the world. The study aimed to identify risk factors and develop a predictive model for ESCC following surgical resection.

Patients and methods

A total of 533 ESCC patients who underwent surgical resection from Suining Central Hospital were enrolled in the study. Cox proportional hazards regression and Least Absolute Shrinkage and Selection Operator (LASSO) regression were performed to identify significant prognostic factors. A prognostic model was constructed, and the receiver operating characteristic (ROC) curve, concordance index (C-index), and decision cure analysis (DCA) were used to evaluate the discrimination and calibration of the prognostic model. Subsequently, we built a nomogram for overall survival (OS) incorporating the prognostic factors, and a calibration plot was employed to assess the consistency between the predicted survival and the observed survival. Based on the model risk score, we split the patients into two subgroups, low-risk and high-risk, and we analyzed the survival time of these two groups using Kaplan–Meier (K-M) survival plots.

Results

Five independent prognosis factors were identified as independent risk factors for OS in ESCC patients who underwent surgical resection. The C-index, ROC curve, and DCA showed that the prognostic model had good predictive accuracy and discriminatory power in the training cohort and validation cohort than other clinical features. A nomogram consisting of prognosis factors showed some superior net benefit. K-M survival plots showed significant differences in OS between the low-risk and high-risk groups. Similar results were observed in the subgroup analysis based on age, grade, and stage. Univariate and multivariate Cox regression analyses revealed that both risk score and risk group are independent prognostic factors in the patient cohort.

Conclusions

This study put forward a novel prognostic model based on clinical features; biopsy data and blood biomarkers may represent a promising tool for estimating OS in ESCC patients.

Nedaplatin-based chemotherapy or cisplatin-based chemotherapy combined with intensity-modulated radiotherapy achieve similar efficacy for stage II-IVa nasopharyngeal carcinoma patients

This retrospective study compared the efficacy and safety of nedaplatin-based chemoradiotherapy and cisplatin-based chemoradiotherapy in stage II-IVa nasopharyngeal carcinoma (NPC) patients. Patients treated with cisplatin-based or nedaplatin-based chemoradiotherapy between January 2012 and December 2015 were evaluated. Survival was estimated by the Kaplan‒Meier method and compared by the log-rank test. Multivariate analysis was performed using the Cox proportional hazards model. A cohort of 538 NPC patients was enrolled. There were no significant differences in the 5-year overall survival (OS), progression-free survival (PFS), locoregional relapse-free survival (LRRFS), or distant metastasis-free survival (DMFS) between the cisplatin and nedaplatin groups. During the whole treatment course, patients in the cisplatin group had higher incidences of grade 3‒4 vomiting and anorexia, while patients in the nedaplatin group had higher incidences of grade 3‒4 leucopenia and mucositis. In terms of late toxicities, patients in the cisplatin group had a higher incidence of xerostomia. In multivariate analysis, T stage, N stage, and clinical stage were prognostic factors for OS, PFS, and DMFS. In subgroup analyses, nedaplatin-based chemotherapy achieved comparable treatment outcomes in specific populations stratified by age, sex, ECOG PS score and clinical stage. Cisplatin and nedaplatin are effective choices for stage II-IVa NPC patients, with a different spectrum of side effects.