The role of radiotherapy in pulmonary large cell neuroendocrine carcinoma: propensity score matching analysis

Unlocking the Radiosensitizing Potential of MYC Inhibition in Neuroendocrine Malignancies

The MYC family of transcription factors-comprising c-MYC, N-MYC, and L-MYC-plays a pivotal role in oncogenesis, driving cancer progression and resistance to therapy. While MYC proteins have long been considered challenging drug targets due to their intricate structures, recent advances have led to the development of promising inhibitors. This review explores the role of MYC overexpression in promoting radiation therapy resistance in aggressive neuroendocrine malignancies through multiple mechanisms, including increased tumor cell invasion, enhanced DNA damage repair and oxidative stress management, prosurvival autophagy, survival of circulating tumor cells, angiogenesis, awakening from dormancy, and modulation of chronic inflammation and host immunity. Paradoxically, MYC overexpression can also enhance radiosensitivity in certain cancer cells by driving proapoptotic pathways, such as reactive oxygen species-induced DNA damage that overwhelms cellular repair mechanisms, ultimately leading to cell death. Additionally, we provide a comprehensive summary of direct MYC inhibitors, detailing their current stage of preclinical and clinical development as novel anticancer therapeutics. This review highlights the role of MYC in cancer metastasis and radiation therapy resistance while examining the potential of MYC inhibitors as radiosensitizers in adult and pediatric neuroendocrine malignancies, including small cell lung cancer, large cell neuroendocrine lung cancer, Merkel cell carcinoma, neuroendocrine-differentiated prostate cancer, neuroblastoma, central nervous system embryonal tumors, and medulloblastoma.

Survival advantage of radiotherapy and nomogram for patients with pulmonary neuroendocrine neoplasms: a propensity score-matched Surveillance, Epidemiology, and End Results database study

Background

The standard treatment for pulmonary neuroendocrine neoplasms (pNENs) is surgery in the early stage and is generally determined according to the histologic type and stage. Radiotherapy (RT) is a treatment option for locally advanced or unresectable lung cancers. The aim of this study was to determine the prognostic value of RT in patients with pNENs using data from the Surveillance, Epidemiology, and End Results database.

Methods

We used propensity score matching analysis to balance differences in variables between the RT and no-RT groups. Univariate and multivariate Cox proportional hazards regression analyses were used to evaluate the factors related to overall survival and cancer-specific survival (CSS). A novel nomogram was constructed, and the results were evaluated using the concordance index.

Results

A total of 7,470 cases were identified between 2000 and 2019, among whom 1,429 were placed in the RT group and propensity-score matched with those in the no-RT group at a 1:1 ratio. Age, sex, marital status, disease extension, surgery, and RT were identified as independent prognostic factors of outcome. There was no significant difference in overall or CSS between RT and no-RT patients in the surgery group (P=0.22 and P=0.72, respectively). However, RT was associated with survival benefit in the no-surgery group. According to the concordance index, the nomogram could accurately predict the prognosis of patients with pNENs.

Conclusions

Our findings indicate that RT may provide a survival benefit for patients with pNENs, particularly for those who did not undergo surgery. The nomogram produced in this study may be a used to predict the prognosis of this patient group.

Implementing machine learning to predict survival outcomes in patients with resected pulmonary large cell neuroendocrine carcinoma

BACKGROUND

The post-surgical prognosis for Pulmonary Large Cell Neuroendocrine Carcinoma (PLCNEC) patients remains largely unexplored. Developing a precise prognostic model is vital to assist clinicians in patient counseling and creating effective treatment strategies.

RESEARCH DESIGN AND METHODS

This retrospective study utilized the Surveillance, Epidemiology, and End Results database from 2000 to 2018 to identify key prognostic features for Overall Survival (OS) in PLCNEC using Boruta analysis. Predictive models employing XGBoost, Random Forest, Decision Trees, Elastic Net, and Support Vector Machine were constructed and evaluated based on Area Under the Receiver Operating Characteristic Curve (AUC), calibration plots, Brier scores, and Decision Curve Analysis (DCA).

RESULTS

Analysis of 604 patients revealed eight significant predictors of OS. The Random Forest model outperformed others, with AUC values of 0.765 and 0.756 for 3 and 5-year survival predictions in the training set, and 0.739 and 0.706 in the validation set, respectively. Its superior validation cohort performance was confirmed by its AUC, calibration, and DCA metrics.

CONCLUSIONS

This study introduces a novel machine learning-based prognostic model with a supportive web-based platform, offering valuable tools for healthcare professionals. These advancements facilitate more personalized clinical decision-making for PLCNEC patients following primary tumor resection.

Early death incidence and prediction in stage IV large cell neuroendocrine carcinoma of the lung

Nearly half of lung large cell neuroendocrine carcinoma (LCNEC) patients are diagnosed at an advanced stage and face a high early death risk. Our objective was to develop models for assessing early death risk in stage IV LCNEC patients. We used surveillance, epidemiology, and end results (SEER) databases to gather data on patients with stage IV LCNEC to construct models and conduct internal validation. Additionally, we collected a dataset from the Second Affiliated Hospital of Nanchang University for external validation. We used the Pearson correlation coefficient and variance inflation factor to identify collinearity among variables. Logistic regression analysis and least absolute shrinkage and selection operator analysis were employed to identify important independent prognostic factors. Prediction nomograms and network-based probability calculators were developed. The accuracy of the nomograms was evaluated using receiver operating characteristic curves. The goodness of fit of the nomograms was evaluated using the Hosmer-Lemeshow test and calibration curves. The clinical value of the models was assessed through decision curve analysis. We enrolled 816 patients from the surveillance, epidemiology, and end results database and randomly assigned them to a training group and a validation group at a 7:3 ratio. In the training group, we identified 9 factors closely associated with early death and included them in the prediction nomograms. The overall early death model achieved an area under the curve of 0.850 for the training group and 0.780 for the validation group. Regarding the cancer-specific early death model, the area under the curve was 0.853 for the training group and 0.769 for the validation group. The calibration curve and Hosmer-Lemeshow test both demonstrated a high level of consistency for the constructed nomograms. Additionally, decision curve analysis further confirmed the substantial clinical utility of the nomograms. We developed a reliable nomogram to predict the early mortality risk in stage IV LCNEC patients that can be a helpful tool for health care professionals to identify high-risk patients and create personalized treatment plans.

Establishment and Validation of Prognostic Nomograms for Patients with Metastatic Pulmonary Large Cell Neuroendocrine Carcinoma

PURPOSE

Metastatic pulmonary large cell neuroendocrine carcinoma (LCNEC) is an aggressive cancer with generally poor outcomes. Effective methods for predicting survival in patients with metastatic LCNEC are needed. This study aimed to identify independent survival predictors and develop nomograms for predicting survival in patients with metastatic LCNEC.

PATIENTS AND METHODS

We conducted a retrospective analysis using the Surveillance, Epidemiology, and End Results (SEER) database, identifying patients with metastatic LCNEC diagnosed between 2010 and 2017. To find independent predictors of cancer-specific survival (CSS), we performed Cox regression analysis. A nomogram was developed to predict the 6-, 12-, and 18-month CSS rates of patients with metastatic LCNEC. The concordance index (C-index), area under the receiver operating characteristic (ROC) curves (AUC), and calibration curves were adopted with the aim of assessing whether the model can be discriminative and reliable. Decision curve analyses (DCAs) were used to assess the model's utility and benefits from a clinical perspective.

RESULTS

This study enrolled a total of 616 patients, of whom 432 were allocated to the training cohort and 184 to the validation cohort. Age, T staging, N staging, metastatic sites, radiotherapy, and chemotherapy were identified as independent prognostic factors for patients with metastatic LCNEC based on multivariable Cox regression analysis results. The nomogram showed strong performance with C-index values of 0.733 and 0.728 for the training and validation cohorts, respectively. ROC curves indicated good predictive performance of the model, with AUC values of 0.796, 0.735, and 0.736 for predicting the 6-, 12-, and 18-month CSS rates of patients with metastatic LCNEC in the training cohort, and 0.795, 0.801, and 0.780 in the validation cohort, respectively. Calibration curves and DCAs confirmed the nomogram's reliability and clinical utility.

CONCLUSION

The new nomogram was developed for predicting CSS in patients with metastatic LCNEC, providing personalized risk evaluation and aiding clinical decision-making.

[Development and Validation of A Prognostic Nomogram to Guide Decision-making in Lung Large Cell Neuroendocrine Carcinoma]

BACKGROUND

Lung large cell neuroendocrine carcinoma (LCNEC) is a rare and highly malignant lung tumor with a poor prognosis. Currently, most research on LCNEC is based on retrospective studies and lacks validation in the real world. The study aims to identify independent risk factors and establish and validate a predictive model for the prognosis of LCNEC.

METHODS

Patient data were extracted from Surveillance, Epidemiology, and End Results (SEER) and our department's hospitalization records from 2010 to 2015 and 2016 to 2020, respectively. Kaplan-Meier analysis was used to evaluate overall survival (OS). OS is defined as the time from diagnosis to death or last follow-up for a patient. Univariate and multivariate Cox regression analyses were performed to identify significant prognostic factors and construct a Nomogram for predicting the prognosis of LCNEC.

RESULTS

In total, 1892 LCNEC patients were included and divided into a training cohort (n=1288) and two validation cohorts (n=552, n=52). Univariate Cox regression analysis showed that age, gender, primary tumor site, laterality, T stage, N stage, M stage, surgery, and radiotherapy were factors that could affect the prognosis of LCNEC patients (P<0.05). Multivariate Cox analysis indicated that age, gender, primary tumor site, T stage, N stage, M stage, surgery, and radiotherapy were independent risk factors for the prognosis of LCNEC patients (P<0.05). Calibration curves and the concordance index (internal: 0.744±0.015; external: 0.763±0.020, 0.832±0.055) demonstrated good predictive performance of the model.

CONCLUSIONS

Patients aged ≥65 years, male, with advanced tumor-node-metastasis (TNM) staging, and who have not undergone surgery or radiotherapy have a poor prognosis. Nomogram can provide a certain reference for personalized clinical decision-making for patients.

Large Cell Neuroendocrine Carcinoma of the Lung: Current Understanding and Challenges

Large cell neuroendocrine carcinoma of the lung (LCNEC) is a rare and highly aggressive type of lung cancer, with a complex biology that shares similarities with both small-cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC). The prognosis of LCNEC is poor, with a median overall survival of 8-12 months. The diagnosis of LCNEC requires the identification of neuroendocrine morphology and the expression of at least one of the neuroendocrine markers (chromogranin A, synaptophysin or CD56). In the last few years, the introduction of next-generation sequencing allowed the identification of molecular subtypes of LCNEC, with prognostic and potential therapeutic implications: one subtype is similar to SCLC (SCLC-like), while the other is similar to NSCLC (NSCLC-like). Because of LCNEC rarity, most evidence comes from small retrospective studies and treatment strategies that are extrapolated from those adopted in patients with SCLC and NSCLC. Nevertheless, limited but promising data about targeted therapies and immune checkpoint inhibitors in patients with LCNEC are emerging. LCNEC clinical management is still controversial and standardized treatment strategies are currently lacking. The aim of this manuscript is to review clinical and molecular data about LCNEC to better understand the optimal management and the potential prognostic and therapeutic implications of molecular subtypes.

Risk factors, survival analysis, and nomograms for distant metastasis in patients with primary pulmonary large cell neuroendocrine carcinoma: A population-based study

INTRODUCTION

Pulmonary large cell neuroendocrine carcinoma (LCNEC) is a rapidly progressive and easily metastatic high-grade lung cancer, with a poor prognosis when distant metastasis (DM) occurs. The aim of our study was to explore risk factors associated with DM in LCNEC patients and to perform survival analysis and to develop a novel nomogram-based predictive model for screening risk populations in clinical practice.

METHODS

The study cohort was derived from the Surveillance, Epidemiology, and End Results database, from which we selected patients with LCNEC between 2004 to 2015 and formed a diagnostic cohort (n = 959) and a prognostic cohort (n = 272). The risk and prognostic factors of DM were screened by univariate and multivariate analyses using logistic and Cox regressions, respectively. Then, we established diagnostic and prognostic nomograms using the data in the training group and validated the accuracy of the nomograms in the validation group. The diagnostic nomogram was evaluated using receiver operating characteristic curves, decision curve analysis curves, and the GiViTI calibration belt. The prognostic nomogram was evaluated using receiver operating characteristic curves, the concordance index, the calibration curve, and decision curve analysis curves. In addition, high- and low-risk groups were classified according to the prognostic monogram formula, and Kaplan-Meier survival analysis was performed.

RESULTS

In the diagnostic cohort, LCNEC close to bronchus, with higher tumor size, and with higher N stage indicated higher likelihood of DM. In the prognostic cohort (patients with LCNEC and DM), men with higher N stage, no surgery, and no chemotherapy had poorer overall survival. Patients in the high-risk group had significantly lower median overall survival than the low-risk group.

CONCLUSION

Two novel established nomograms performed well in predicting DM in patients with LCNEC and in evaluating their prognosis. These nomograms could be used in clinical practice for screening of risk populations and treatment planning.

Clinical features and treatment outcomes of resected large cell neuroendocrine carcinoma of the lung

PURPOSE

Pulmonary large cell neuroendocrine carcinoma (LCNEC) is a high-grade lung neuroendocrine tumor with a poor prognosis, similar to small cell lung cancer (SCLC). However, it remains unclear whether to treat LCNEC as non-small-cell lung cancer (NSCLC) or as SCLC. We reviewed our experiences to suggest appropriate treatment strategy for resected pulmonary LCNEC.

MATERIALS AND METHODS

Forty-four patients were treated for pathologically diagnosed pulmonary LCNEC during 2005‒2018. We considered curative surgery first in early-stage or some locally advanced tumors, unless medically inoperable. Adjuvant treatments were decided considering patient's clinical and pathological features. After excluding two stage I tumors with radiotherapy alone and three stage III tumors with upfront chemotherapy, we analyzed 39 patients with stage I‒III pulmonary LCNEC, who underwent curative resection first.

RESULTS

Adjuvant chemotherapy (NSCLC-based 91%, SCLC-based 9%) was performed in 62%, and adjuvant radiotherapy was done in three patients for pN2 or positive margin. None received prophylactic cranial irradiation (PCI). With a median follow-up of 30 months, the 2- and 5-year overall survival (OS) rates were 68% and 51%, and the 2- and 5-year recurrence-free survival (RFS) rates were 49% and 43%, respectively. Aged ≥67 years and SCLC-mixed pathology were significant poor prognostic factors for OS or RFS (p < 0.05). Among 17 recurrences, regional failures were most common (n = 6), and there were five brain metastases.

CONCLUSIONS

Surgery and adjuvant treatment (without PCI) could achieve favorable outcomes in pulmonary LCNEC, which was more similar to NSCLC, although some factors worsened the prognosis. The importance of intensified adjuvant therapies with multidisciplinary approach remains high.