Outcomes of Patients with Pulmonary Large Cell Neuroendocrine Carcinoma in I-IV Stage

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.

Prognostic factors and predictive models for patients with lung large cell neuroendocrine carcinoma: Based on SEER database

BACKGROUND

Lung Large cell neuroendocrine carcinoma (LCNEC) is a rare, aggressive, high-grade neuroendocrine carcinoma with a poor prognosis, mainly seen in elderly men. To date, we have found no studies on predictive models for LCNEC.

METHODS

We extracted data from the Surveillance, Epidemiology, and End Results (SEER) database of confirmed LCNEC from 2010 to 2018. Univariate and multivariate Cox proportional risk regression analyses were used to identify independent risk factors, and then we constructed a novel nomogram and assessed the predictive effectiveness by receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).

RESULTS

A total of 2546 patients with LCNEC were included, excluding those diagnosed with autopsy or death certificate, tumor, lymph node, metastasis (TNM) stage, tumor grade deficiency, etc., and finally, a total of 743 cases were included in the study. After univariate and multivariate analyses, we concluded that the independent risk factors were N stage, intrapulmonary metastasis, bone metastasis, brain metastasis, and surgical intervention. The results of ROC curves, calibration curves, and DCA in the training and validation groups confirmed that the nomogram could accurately predict the prognosis.

CONCLUSIONS

The nomogram obtained from our study is expected to be a useful tool for personalized prognostic prediction of LCNEC patients, which may help in clinical decision-making.

Efficacy of immune checkpoint inhibitors in advanced large cell neuroendocrine carcinoma of the lung: A single‑institution experience

Large cell neuroendocrine carcinoma (LCNEC) is a rare and highly invasive lung cancer subtype with an overall poor prognosis. Due to its low incidence rate and unusual pathological features, the clinical management of LCNEC remains controversial. The present study aimed to assess the effect of immune checkpoint inhibitors (ICIs) on treatment response and survival outcomes in patients with advanced LCNEC. The clinical data from 148 patients with LCNEC treated with ICIs at The First Affiliated Hospital of Zhengzhou University (Zhengzhou, China) between January 2019 and September 2021 were retrospectively analyzed. Kaplan-Meier and multivariable Cox regression analyses were used to evaluate associations between clinicopathological variables and patient outcomes. Patients treated with ICIs demonstrated extended median overall survival (mOS) times [23.5 months; 95% confidence interval (CI), 18.524-28.476] compared with patients who did not receive ICIs (11.2 months; 95% CI, 4.530-18.930) (P<0.001). Univariate analysis revealed that histological subtype (P=0.043), lymph node metastases (P=0.032) and number of metastatic organs (P=0.009) were associated with a poor prognosis. The heterogeneity of pathological components was associated with prognosis, and the mOS time was shorter for mixed LCNEC than that for pure LCNEC (P=0.043). The median progression-free survival (mPFS) (9.78 vs. 9.37 months; P=0.82) and mOS (20.70 vs. 25.79 months; P=0.181) times showed no significant association with regard to different regimens of immuno-based combination therapy (chemotherapy combined with ICIs vs. anti-angiogenic agents combined with ICIs). Poor Eastern Cooperative Oncology Group performance status score (P=0.04), multiple organ metastases (P=0.02) and high cancer antigen 125 levels (P=0.01) were independent risk factors of a poor prognosis. The present findings offer valuable insights into potential prognostic markers and highlight the favorable impact of ICIs on OS in advanced LCNEC. Prospective clinical studies are required to validate the therapeutic value of ICIs in LCNEC.

The outcomes of different regimens depend on the molecular subtypes of pulmonary large-cell neuroendocrine carcinoma: A retrospective study in China

BACKGROUND

The optimal systemic treatment for pulmonary large-cell neuroendocrine carcinoma (LCNEC) remains controversial, and recent advances in LCNEC molecular subtype classification have provided potential strategies for assisting in treatment decisions. Our study aimed to investigate the impact of treatment regimens, molecular subtypes and their concordance on clinical outcomes of patients diagnosed with LCNEC.

PATIENTS AND METHODS

All patients diagnosed with advanced pulmonary LCNEC in Peking Union Medical College Hospital (PUMCH) between January 2000 and October 2021 were enrolled in this retrospective study. The tumor samples were collected and sequenced using a tumor-specific gene panel, while clinical information was retrieved from the medical records system. The survival and therapeutic response were analyzed and compared between different subgroups classified by treatment regimen (SCLC or NSCLC-based), molecular subtype (type I or II) or the combination.

RESULTS

In univariate subgroup analysis categorized only by treatment regimen or molecular subtype, there were no differences identified in DCR, ORR, PFS, or OS. Nevertheless, the group with consistent treatment regimen and molecular subtype exhibited significantly longer OS than that of the inconsistent group (median OS 37.7 vs. 8.3 months; p = 0.046). Particularly, the OS of patients with type II LCNEC treated with SCLC-based regimen was significantly prolonged than that of others (median 37.7 vs. 10.5 months; p = 0.039).

CONCLUSIONS

Collectively, our study revealed the clinical outcomes of different treatment regimens for LCNEC patients highly depend on their molecular subtypes, highlighting the need for sequencing-guided therapy.

Novel Mouse Cell Lines and In Vivo Models for Human High-Grade Neuroendocrine Lung Carcinoma, Small Cell Lung Carcinoma (SCLC), and Large Cell Neuroendocrine Carcinoma (LCNEC)

There is a clear need to expand the toolkit of adequate mouse models and cell lines available for preclinical studies of high-grade neuroendocrine lung carcinoma (small cell lung carcinoma (SCLC) and large cell neuroendocrine carcinoma (LCNEC)). SCLC and LCNEC are two highly aggressive tumor types with dismal prognoses and few therapeutic options. Currently, there is an extreme paucity of material, particularly in the case of LCNEC. Given the lack of murine cell lines and transplant models of LCNEC, the need is imperative. In this study, we generated and examined new models of LCNEC and SCLC transplantable cell lines derived from our previously developed primary mouse LCNEC and SCLC tumors. RNA-seq analysis demonstrated that our cell lines and syngeneic tumors maintained the transcriptome program from the original transgenic primary tumor and displayed strong similarities to human SCLC or LCNEC. Importantly, the SCLC transplanted cell lines showed the ability to metastasize and mimic this characteristic of the human condition. In summary, we generated mouse cell line tools that allow further basic and translational research as well as preclinical testing of new treatment strategies for SCLC and LCNEC. These tools retain important features of their human counterparts and address the lack of LCNEC disease models.

Differences between Advanced Large Cell Neuroendocrine Carcinoma and Advanced Small Cell Lung Cancer: A Propensity Score Matching Analysis

Background: Nowadays, the characteristics and treatment of advanced pulmonary large cell neuroendocrine carcinoma (LCNEC) remain controversial. This study aimed to analyze the similarity of clinical characteristics, survival outcomes and treatment modalities between advanced LCNEC and advanced small cell lung cancer (SCLC) to provide more evidence for the study of advanced LCNEC. Methods: All SCLC and LCNEC patient data were obtained from the SEER database (2010-2019). Pearson's χ2 test was used to compare the differences in clinical characteristics. Propensity score matching (PSM) was utilized to balance the bias of the variables between patients. Univariate and multivariate Cox proportional hazards regression analyses were performed to identify prognostic factors. KM analysis was used to calculate survival. Results: A total of 1094 patients with IV LCNEC and 20939 patients with IV SCLC were included in this study. The demographic characteristics and tumor characteristics of IV LCNEC and IV SCLC were different (p < 0.05). After PSM, the overall survival (OS) for IV LCNEC and IV SCLC was 6.0 months, the cancer-specific survival (CSS) was 7.0 months, and there was no significant difference in OS or CSS between the two groups. Risk/protective factors for OS and CSS were similar for IV LCNEC and IV SCLC patients. Survival outcomes were similar in patients with IV LCNEC and IV SCLC with different treatment modalities; chemoradiotherapy significantly improved OS and CSS in patients with IV LCNEC (9.0 months) and SCLC (10.0 months), however, radiotherapy alone did not improve survival in patients with IV LCNEC. Conclusions: These results confirmed that the prognosis and treatment modalities are similar and that advanced LCNEC could be treated as advanced SCLC, which provide new evidence for the treatment of advanced LCNEC patients.

Recurrence-Free Survival in Early and Locally Advanced Large Cell Neuroendocrine Carcinoma of the Lung after Complete Tumor Resection

BACKGROUND

Large Cell Neuroendocrine Carcinoma (LCNEC) is a rare subtype of lung cancer with poor clinical outcomes. Data on recurrence-free survival (RFS) in early and locally advanced pure LCNEC after complete resection (R0) are lacking. This study aims to evaluate clinical outcomes in this subgroup of patients and to identify potential prognostic markers.

METHODS

Retrospective multicenter study including patients with pure LCNEC stage I-III and R0 resection. Clinicopathological characteristics, RFS, and disease-specific survival (DSS) were evaluated. Univariate and multivariate analyses were performed.

RESULTS

39 patients (M:F = 26:13), with a median age of 64 years (44-83), were included. Lobectomy (69.2%), bilobectomy (5.1%), pneumonectomy (18%), and wedge resection (7.7%) were performed mostly associated with lymphadenectomy. Adjuvant therapy included platinum-based chemotherapy and/or radiotherapy in 58.9% of cases. After a median follow-up of 44 (4-169) months, the median RFS was 39 months with 1-, 2- and 5-year RFS rates of 60.0%, 54.6%, and 44.9%, respectively. Median DSS was 72 months with a 1-, 2- and 5-year rate of 86.8, 75.9, and 57.4%, respectively. At multivariate analysis, age (cut-off 65 years old) and pN status were independent prognostic factors for both RFS (HR = 4.19, 95%CI = 1.46-12.07, p = 0.008 and HR = 13.56, 95%CI 2.45-74.89, p = 0.003, respectively) and DSS (HR = 9.30, 95%CI 2.23-38.83, p = 0.002 and HR = 11.88, 95%CI 2.28-61.84, p = 0.003, respectively).

CONCLUSION

After R0 resection of LCNEC, half of the patients recurred mostly within the first two years of follow-up. Age and lymph node metastasis could help to stratify patients for adjuvant therapy.

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.