Prognostic nomogram predicts overall survival in pulmonary large cell neuroendocrine carcinoma

Pulmonary Large-Cell Neuroendocrine Carcinoma, a Multifaceted Disease-Case Report and Literature Review

Background and Clinical Significance: This article explores the complexity of large-cell neuroendocrine carcinoma (LCNEC) by presenting a clinical case involving a 17-year-old admitted for persistent wheezing, with no history of respiratory toxin exposure, a background of atopy, and a suspected diagnosis of bronchial asthma. Given the patient's age and the nature of the symptoms, the condition was initially diagnosed as asthma, leading to the initiation of maximum inhalation therapy. Case Presentation: Despite proper adherence and correct administration, symptoms persisted, necessitating the use of oral corticosteroids. Imaging revealed an extensive inhomogeneous mass in the cervical esophagus and trachea, along with a similar tumor in the right hilum, prompting bronchoscopy. The diagnosis of LCNEC was confirmed through imaging, histopathological findings, and a detailed immunohistochemical profile. Initially misdiagnosed as adenoid cystic carcinoma, this case highlights the diagnostic challenges and the importance of rigorous evaluation. Conclusions: It emphasizes that recurrent wheezing in adolescents is not always indicative of asthma and requires careful differential diagnosis to uncover less common causes.

[Research Progress on Molecular Subtypes and Precision Therapy of Pulmonary Large Cell Neuroendocrine Carcinoma]

Pulmonary large cell neuroendocrine carcinoma (LCNEC) is a high-grade neuroendocrine tumor with unique characteristics, and its treatment regimens are primarily derived from those for small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC). In recent years, the incidence rate has been on the rise, and the prognosis are affected by the interaction of multiple factors such as individual, clinical stage and treatment mode, and the heterogeneity is significant. In the study of molecular subtypes, multiple subgroups were divided according to key gene mutations such as RB1 and TP53, and genomic subtypes were associated with survival, chemotherapy response, and efficacy of precision therapy. Targeted therapy excavates multiple targets, and the efficacy of drugs is different. Immunotherapy has made remarkable progress, and immune checkpoint inhibitors (ICIs) have been effective in all stages of chemotherapy alone or in combination with chemotherapy or radiation therapy, but there is a risk of hyperprogressive diseases, and accurate prognostic markers need to be explored urgently. This review reviews the latest research progress in the study of molecular subtypes and precision therapies such as targeted therapy and immunotherapy of pulmonary LCNEC, and points out that pulmonary LCNEC treatment will develop in the direction of precision and individualization in the future.
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Rachel score: a nomogram model for predicting the prognosis of lung neuroendocrine tumors

BACKGROUND

Lung NET, classified in typical carcinoids (TC) and atypical carcinoids (AC), are highly heterogeneous in their biology and prognosis. The histological subtype and TNM stage are well-established prognostic factors for lung NET. In a previous work by our group, we demonstrated a significant impact of laterality on lung NET survival outcomes.

MATERIALS AND METHODS

We developed a nomogram that integrates relevant prognostic factors to predict lung NET outcomes. By adding the scores for each of the variables included in the model, it was possible to obtain a prognostic score (Rachel score). Wilcoxon non-parametric statistical test was applied among parameters and Harrell's concordance index was used to measure the models' predictive power. To test the discriminatory power and the predictive accuracy of the model, we calculated Gonen and Heller concordance index. Time-dependent ROC curves and their area under the curve (AUC) were used to evaluate the models' predictive performance.

RESULTS

By applying Rachel score, we were able to identify three prognostic groups (specifically, high, medium and low risk). These three groups were associate to well-defined ranges of points according to the obtained nomogram (I: 0-90, II: 91-130; III: > 130 points), providing a useful tool for prognostic stratification. The overall survival (OS) and progression free survival (PFS) Kaplan-Meier curves confirmed significant differences (p < 0.0001) among the three groups identified by Rachel score.

CONCLUSIONS

A prognostic nomogram was developed, incorporating variables with significant impact on lung NET survival. The nomogram showed a satisfactory and stable ability to predict OS and PFS in this population, confirming the heterogeneity beyond the histopathological diagnosis of TC vs AC.

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.

[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.

Lung Large Cell Neuroendocrine Carcinoma: A Population-Based Retrospective Cohort Study

BACKGROUNDS

Pulmonary large cell neuroendocrine carcinoma (LCNEC) is a rarely high-grade neuroendocrine carcinoma of the lung with features of both small cell and non-small cell lung cancer. In this study, we aim to construct a prognostic nomogram that integrates the clinical features and treatment options to predict disease-specific survival (DSS).

METHODS

A total of 713 patients diagnosed with LCNEC were from the US National Cancer Institute's Surveillance Epidemiology and End Results (SEER) registry between 2010-2016. Cox proportional hazards analysis was conducted to choose the significant predictors of DSS. External validation was performed using 77 patients with LCNEC in the West China Hospital Sichuan University between 2010-2018. The predictive accuracy and discriminative capability were estimated by the concordance index (C-index), calibration curve, and receiver operating characteristic (ROC) curve. The clinical applicability of the nomogram was verified through the decision curve analysis (DCA). Additionally, we conducted a subgroup analysis of data available in the external cohort that may impact prognosis but was not recorded in the SEER database.

RESULTS

Six independent risk factors for DSS were identified and integrated into the nomogram. The nomogram achieved good C- indexes of 0.803 and 0.767 in the training and validation group, respectively. Moreover, the calibration curves for the probability of survival showed good agreement between prediction by nomogram and actual observation in 1-, 3- and 5-year DSS. The ROC curves demonstrated the prediction accuracy of the established nomogram (all Area Under Curve (AUC) > 0.8). DCA exhibited the favorable clinical applicability of the nomogram in the prediction of LCNEC survival. A risk classification system was built which could perfectly classify LCNEC patients into high-, medium- and low-risk groups (p < 0.001). The survival analysis conducted on the West China Hospital cohort indicated that whole brain radiation therapy (WBRT), prophylactic cranial irradiation (PCI), surgical procedures, tumor grade, Ki-67, and PD-L1 expression were not significantly associated with DSS.

CONCLUSION

This study has effectively developed a prognostic nomogram and a corresponding risk stratification system, which demonstrate promising potential for predicting the DSS of patients with LCNEC.

Incidence and prognostic nomogram for resected non-small cell neuroendocrine tumor: A population-based respective study in China and the SEER database

Background

Pulmonary neuroendocrine tumors, including small cell lung cancer (SCLC) and non-small cell neuroendocrine tumor (NSCLC-NET), have obvious heterogeneity. The comparison between SCLC and NSCLC-NET, and prognostic nomogram of resected NSCLC-NET have not been performed.

Methods

We retrieved data from SEER database. The incidence and prognostic factors were compared between SCLC and NSCLC-NET. By Cox regression, we constructed prognostic nomogram of resected NSCLC-NET. The nomogram was evaluated by ROC, calibration plot and decision curve analysis (DCA) and compared with 8th TNM staging system. A Chinese cohort was used for external validation.

Results

The age-adjusted incidence of SCLC declined after 1991 but the incidence of NSCLC-NET continuously rose. Patients with typical carcinoid had the best prognosis in both overall survival and lung cancer specific survival, followed by atypical carcinoid, large cell neuroendocrine tumor and SCLC after operation. Patients receiving sleeve resection in NSCLC-NET had longer survival but segmental resection was more recommended in SCLC. High-smoking index was associated with worse overall survival in both SCLC and NSCLC-NET. Histological subtype, age, surgery type, N, M stage and chemotherapy were independent prognostic factors and used to construct prognostic nomogram of resected NSCLC-NET. The nomogram performed well with good discrimination, calibration and clinical usefulness, which was validated by a Chinese cohort (1, 3, 5-year AUC: SEER cohort 0.873, 0.901, 0.875; Chinese cohort 0.867, 0.892, 0.874). Compared to the 8th staging system, the nomogram had higher C-index (0.87 vs 0.728, P < 0.001), clinical usefulness, increasing AUC value over time and improved 68%.

Conclusion

The prognostic nomogram of resected NSCLC-NET performed better than the 8th TNM staging system. It may have certain value in risk stratification and survival prediction of patients with resected NSCLC-NET and help clinicians to take measures for high-risk patients in advance.

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.

A Clinical Nomogram for Predicting Cancer-Specific Survival in Pulmonary Large-Cell Neuroendocrine Carcinoma Patients: A Population-Based Study

Purpose

This study was designed to construct and validate a nomogram that was available for predicting cancer-specific survival (CSS) in patients with pulmonary large-cell neuroendocrine carcinoma (LCNEC).

Patients and Methods

Using the US Surveillance, Epidemiology, and End Results (SEER) database, we identified patients pathologically diagnosed as LCNEC from 1975 to 2016. Univariate and multivariate Cox regression was conducted to assess prognostic factors of CSS. A novel nomogram model was constructed and validated by the concordance index (C-index), calibration curves and decision curve analysis (DCA).

Results

A total of 624 LCNEC patients were enrolled. Five prognostic factors for CSS were identified and merged to establish nomograms. In the training and validation cohorts, calibration curves displayed the nomogram predictions are in a good agreement with the actual survival. The C-Index of the training and validation cohorts were both higher than 0.8, and the DCA results showed that the nomogram has clinical validity and utility.

Conclusion

The proposed nomogram resulted in accurate CSS prognostic prediction for patients with LCNEC.

Establishment of a Macrophage Phenotypic Switch Related Prognostic Signature in Patients With Pancreatic Cancer

Introduction

Macrophage phenotype switch plays a vital role in the progression of malignancies. We aimed to build a prognostic signature by exploring the expression pattern of macrophage phenotypic switch related genes (MRGs) in the Cancer Genome Atlas (TCGA)—pancreatic adenocarcinoma (PAAD), Genotype-Tissue Expression (GTEx)-Pancreas, and Gene Expression Omnibus (GEO) databases.

Methods

We identified the differentially expressed genes between the PAAD and normal tissues. We used single factor Cox proportional risk regression analysis, Least Absolute Shrinkage and Selection Operator (LASSO) analysis, and multivariate Cox proportional hazard regression analysis to establish the prognosis risk score by the MRGs. The relationships between the risk score and immune landscape, “key driver” mutations and clinicopathological factors were also analyzed. Gene-set enrichment analysis (GSEA) analysis was also performed.

Results

We detected 198 differentially expressed MRGs. The risk score was constructed based on 9 genes (KIF23, BIN1, LAPTM4A, ERAP2, ATP8B2, FAM118A, RGS16, ELMO1, RAPGEFL1). The median overall survival time of patients in the low-risk group was significantly longer than that of patients in the high-risk group (P < 0.001). The prognostic value of the risk score was validated in GSE62452 dataset. The prognostic performance of nomogram based on risk score was superior to that of TNM stage. And GSEA analysis also showed that the risk score was closely related with P53 signaling pathway, pancreatic cancer and T cell receptor signaling pathway. qRT-PCR assay showed that the expressions of the 9 MRGs in PDAC cell lines were higher than those in human pancreatic ductal epithelium cell line.

Conclusions

The nine gene risk score could be used as an independent prognostic index for PAAD patients. Further studies validating the prognostic value of the risk score are warranted.

A nomogram prognostic model for large cell lung cancer: analysis from the Surveillance, Epidemiology and End Results Database

Background

Currently, there is no reliable method for predicting the prognosis of patients with large cell lung cancer (LCLC). The aim of this study was to develop and validate a nomogram model for accurately predicting the prognosis of patients with LCLC.

Methods

LCLC patients, diagnosed from 2007 to 2009, were identified from the Surveillance, Epidemiology and End Results (SEER) database and used as the training dataset. Significant clinicopathologic variables (P<0.05) in a multivariate Cox regression were selected to build the nomogram. The performance of the nomogram model was evaluated by the concordance index (C-index), the area under the curve (AUC), and internal calibration. LCLC patients diagnosed from 2010 to 2016 in the SEER database were selected as a testing dataset for external validation. The nomogram model was also compared with the currently used American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) staging system (8^th edition) by using C-index and a decision curve analysis.

Results

Eight variables-age, sex, race, marital status, T stage, N stage, M stage, and treatment strategy-were statistically significant in the multivariate Cox model and were selected to develop the nomogram model. This model exhibited excellent predictive performance. The C-index and AUC value were 0.761 [95% confidence interval (CI), 0.754 to 0.768] and 0.886 for the training dataset and 0.773 (95% CI, 0.765 to 0.781) and 0.876 for the testing dataset, respectively. This model also predicted three-year and five-year lung cancer-specific survival (LCSS) in both datasets with good fidelity. This nomogram model performs significantly better than the 8th edition AJCC TNM staging system, with a higher C-index (P<0.001) and better net benefits in predicting LCSS in LCLC patients.

Conclusions

We developed and validated a prognostic nomogram model for predicting 3- and 5-year LCSS in LCLC patients with good discrimination and calibration abilities. The nomogram may be useful in assisting clinicians to make individualized decisions for appropriate treatment in LCLC.

Upregulation of Long Non-Coding RNA GCC2-AS1 Facilitates Malignant Phenotypes and Correlated With Unfavorable Prognosis for Lung Adenocarcinoma

Background

The role played by long noncoding RNA GCC2-AS1 in primary malignant tumors remains poorly understood. This study aimed to determine the expression levels and evaluate the clinical significance and biological effects of GCC2-AS1 in lung adenocarcinoma (LUAD).

Methods

We used data obtained from tissue samples and the TCGA database to determine the levels of GCC2-AS1 expression LUAD patients, and the prognostic value of those levels. Functional experiments were performed to investigate the effect of GCC2-AS1 on LUAD cells. Genes that were differentially expressed in GCC2-AS1-low and -high groups were analyzed by an enrichment analysis. Additionally, a nomogram model was created and subgroup analyses were performed to further determine the prognostic value of GCC2-AS1 in LUAD.

Results

GCC2-AS1 expression was significantly upregulated in lung adenocarcinoma tissues as compared with normal tissues. Depletion of GCC2-AS1 inhibited the proliferation and invasion of LUAD cells in vitro. An elevated level of GCC2-AS1 was strongly correlated with shorter overall survival time and was identified as an independent prognostic marker for LUAD patients. Enrichment analyses conducted using GO, KEGG, and GSEA databases were performed to identify biological pathways that might involve GCC2-AS1. Several subgroups were found to have a significant prognostic value for patients in the GCC2-AS1-low and -high groups.

Conclusions

Our findings suggest that GCC2-AS1 can serve as a diagnostic and prognostic biomarker for LUAD patients.

Heterogeneous Tumor-Immune Microenvironments between Primary and Metastatic Tumors in a Patient with ALK Rearrangement-Positive Large Cell Neuroendocrine Carcinoma

Evolution of tumor-immune microenviroments (TIMEs) occurs during tumor growth and dissemination. Understanding inter-site tumor-immune heterogeneity is essential to harness the immune system for cancer therapy. While the development of immunotherapy against lung cancer with driver mutations and neuroendocrine tumors is ongoing, little is known about the TIME of large cell neuroendocrine carcinoma (LCNEC) or anaplastic lymphoma kinase (ALK) rearrangement-positive lung cancer. We present a case study of a 32-year-old female patient with ALK-rearrangement-positive LCNEC, who had multiple distant metastases including mediastinal lymph-node, bilateral breasts, multiple bones, liver and brain. Multiple biopsy samples obtained from primary lung and three metastatic tumors were analyzed by fluorescent multiplex immunohistochemistry. Tissue localizations of tumor-infiltrating lymphocytes in the tumor nest and surrounding stroma were evaluated. T cell and B cell infiltrations were decreased with distance from primary lung lesion. Although each tumor displayed a unique TIME, all tumors exhibited concomitant regression after treatment with an ALK-inhibitor. This study provides the first evidence of the coexistence of distinct TIME within a single individual with ALK-rearrangement-positive LCNEC. The present study contributes to our understanding of heterogeneous TIMEs between primary and metastatic lesions and provides new insights into the complex interplay between host-immunity and cancer cells in primary and metastatic lesions.

Pulmonary neuroendocrine neoplasms with well differentiated morphology and high proliferative activity: illustrated by a case series and review of the literature

OBJECTIVES

Pulmonary neuroendocrine neoplasms (NENs) are subdivided in carcinoids and neuroendocrine carcinomas (small cell lung carcinoma and large cell neuroendocrine carcinoma (LCNEC)), based on the presence of necrosis and mitotic index (MI). However, it is unclear if tumors with well differentiated morphology but high proliferation rate should be regarded as LCNEC or as high grade carcinoids. In previous case series, a longer overall survival then expected in LCNEC has been suggested. We describe 7 of those cases analyzed for pRb expression and overall survival.

MATERIAL AND METHODS

Cases with well differentiated morphology, but MI > 10/2mm² and/or Ki-67 proliferation index >20% were selected based on pathology reports of consecutive NENs in our university medical center (Maastricht UMC+, 2007-2018) and confirmed by pathological review. Immunohistochemistry was performed to assess pRb expression.

RESULTS

Seven stage IV cases were included in this study. Median overall survival was 8 months (95% confidence interval 5-11 months). Cases with well differentiated morphology and preserved pRb expression (4/7) had a median overall survival of 45 months.

CONCLUSION

A subgroup of pulmonary NENs with well differentiated morphology but high proliferation rate likely exists. pRb staining might be helpful to predict prognosis, but clinical relevance remains to be studied.

Outcomes of Patients with Clinical Stage I-IIIA Large-Cell Neuroendocrine Lung Cancer Treated with Resection

Large-cell neuroendocrine carcinoma (LCNEC) is a rare malignancy with poor prognosis. The rationale of the study was to determine the survival of LCNEC patients in I–IIIA clinical stages who underwent resection. A total of 53 LCNEC (89%) and combined LCNEC (11%) patients in stages I–IIIA who underwent surgery with radical intent between 2002–2018 were included in the current study. Overall survival (OS) and time to recurrence (TTR) were estimated. Uni- and multivariable analyses were conducted using Cox-regression model. Patients were treated with surgery alone (51%), surgery with radiochemotherapy (4%), with radiotherapy (2%), with adjuvant chemotherapy (41%), or with neoadjuvant chemotherapy (2%). The median (95% Confidence Interval (CI)) OS and TTR was 52 months (20.1–102.1 months) and 20 months (7.0–75.6 months), respectively. Patients treated in clinical stage I showed better OS than patients in stages II–IIIA (p = 0.008). Patients with R0 resection margin (negative margin, no tumor at the margin) and without lymph node metastasis had significantly better TTR. In the multivariate analysis, age was an independent factor influencing OS. Recurrence within 1 year was noted in more than half cases of LCNEC. R0 resection margin and N0 status (no lymph node metastasis) were factors improving TTR. Age >64 years was observed as a main independent factor influencing OS.