Prognostic factors for pulmonary large-cell neuroendocrine carcinoma: a competing-risks analysis

Abstract

Introduction

Pulmonary large-cell neuroendocrine carcinoma (LCNEC) is a rare and highly invasive subtype of lung cancer that accounts for fewer than 3% of cases[1, 2]. LCNEC was first reported as a solitary pulmonary neuroendocrine tumor in 1991[3]. The World Health Organization (WHO) subsequently recognized LCNEC as a variant of large-cell carcinoma and a type of neuroendocrine tumor and non-small-cell lung cancer[4, 5]. However, the 2015 WHO standard classifies LCNEC, small-cell lung carcinoma, typical carcinoid, and atypical carcinoid as neuroendocrine tumors[6]. The low incidence of pulmonary LCNEC has resulted in there being few prognostic studies of pulmonary LCNEC, and moreover the findings of these studies have been controversial.

Previous studies have performed survival analyses of pulmonary LCNEC patients. However, the application of traditional survival analysis methods that are widely used to identify prognostic factors has limitations[7], such as overestimating the risk of disease by failing to allow for competing risk factors for death. The competing-risks model is an analytical technique used to deal with competing events and is being increasingly used in clinical research[8–10]. Moreover, a large-sample study of rare diseases can be conducted by utilizing a population-based cancer database, and the Surveillance, Epidemiology, and End Results (SEER) program of the National Cancer Institute covers approximately 34.6% of the U.S. population. Analyzing the SEER database should provide useful information on the prognostic factors for pulmonary LCNEC.

This study considered other causes of death as competing events for LCNEC-specific death (LCSD). We used a competing-risks model to analyze the survival of pulmonary LCNEC patients in the SEER database in order to screen prognostic factors and provide reliable evidence for clinical treatment decisions.

Methods

2.1. Data sources

The specific database we used is designated “the Incidence—SEER 18 Regs Custom Data (with additional treatment fields), Nov 2018 Sub (1975–2016 varying).” The SEER Research Data Agreement was signed for accessing the information in the SEER database using reference number 11075-Nov2018. All data on patients with pulmonary LCNEC were obtained using version 8.3.5 of the SEER*Stat software (www.seer.cancer.gov/seerstat). Since all information in the SEER database has been de-identified, no institutional review board approval or informed consent was required for this study.

2.2. Patients

Patients were identified in the SEER database as having pulmonary LCNEC by applying the International Classification of Disease—Oncology, Third Edition (ICD-O–3) site code C34.0-C34.9 and the ICD-O–3 histology code 8013/3. The inclusion criteria for this study were as follows: (1) aged ≥18 years and diagnosed between 2004 to 2015; (2) diagnosis confirmed by microscopy; and (3) availability of data on age at diagnosis, race, sex, marital status, year of diagnosis, primary site, laterality, grade, American Joint Committee on Cancer (AJCC) stage, T stage, N stage, M stage, lymph-node status, surgery, radiotherapy, chemotherapy, survival time, and cause of death. Patients with nonprimary tumors were excluded from the study. The patient inclusion and exclusion process applied to the SEER database is shown in Figure 1.

2.3. Covariates

We included the following variables: age at diagnosis, race, sex, marital status, year of diagnosis, primary site, laterality, grade, AJCC stage, T stage, N stage, M stage, lymph-node status, surgery, radiotherapy, chemotherapy, and survival time. Unmarried patients included those who were widowed, single, unmarried, living with a domestic partner, divorced, or separated. The primary tumor site was divided into lung lobe, main bronchus, overlapping lesion of the lung, and not otherwise specified.

The causes of death were divided into the following three situations: alive, LCSD, and death from other causes. LCSD was the primary outcome that we were interested in, and other causes of death were considered competing events.

2.4. Statistical analysis

All tests were two-sided and P<0.05 was considered indicative of statistical significance. Categorical variables are expressed as percentages. Continuous variables that conformed to a normal distribution are expressed as mean and standard-deviation values, while other continuous variables (i.e., those conforming to a skewed distribution) are presented by median and interquartile-range values. We used the CumIncidence function for the univariate analysis, the cumulative incidence function (CIF) for determining the cumulative morbidity at different time points, and Gray’s test for testing different categories[11]. There was a competing risk between our outcomes, and so Cox regression analysis, subdistribution hazard function (SD) analysis, and cause-specific hazard function (CS) analysis were used for the multivariate analysis to identify prognostic factors[12, 13]. The results of the multivariate analysis are presented as hazard ratio (HR) and associated 95% confidence interval (CI) values. All analyses were performed using SAS statistical software (version 9.4).

Results

3.1. Patient Characteristics

We finally screened 1246 patients diagnosed with pulmonary LCNEC; their demographic and tumor characteristics are presented in Table 1. The median survival times in the LCSD and total-patients groups were 10 and 16 months, respectively. The 937 patients who died comprised 796 who died from LCNEC and 141 who died from other causes, which indicates that competing events constituted 15% of the deaths.

The largest age group of the diagnosed total patients comprised those aged 60–80 years, followed by patients younger than 60 years, with patients older than 80 years accounting for only a small proportion. Most of the patients who died were white (84.51%), and male deaths predominated in both the total-patient (54.98%) and LCSD (57.66%) groups. Regarding the tumor origin, in 89% of the patients it was located in a lung lobe, and only 1.36% of patients had overlapping lesions of the lung. AJCC stages I and IV accounted for 37.4% and 32.1% of the total patients, respectively. The proportions of patients with TNM stages T1 and T2 were 27.69% and 38.28%, respectively. TNM stage N0 patients accounted for 52.33%, while lymph-node-positive patients accounted for 38.84%. In addition, 56.02% and 52.17% of patients received surgery and chemotherapy, respectively, whereas only 13.88% received radiotherapy.

3.2. Univariate analysis

The univariate analysis revealed that the prognostic factors for pulmonary LCNEC were sex (P = 0.0005), primary site (P<0.001), laterality (P<0.001), AJCC stage (P<0.001), T stage (P<0.001), N stage (P<0.001), M stage (P<0.001), lymph-node status (P<0.001), surgery (P<0.001), and chemotherapy (P = 0.007) (Figures 2). The 6-, 12-, and 24-month CIFs are presented in Table 2.

3.3. Multivariate analysis

The multivariate analysis indicated that there were too few competing events to allow M-stage patients to be included in the calculations. The Cox regression analysis indicated that the independent prognostic factors for pulmonary LCNEC were sex (P = 0.0017, HR = 0.809, 95%CI = 0.708–0.923), AJCC stage II (P<0.0001, HR = 2.629, 95%CI = 1.826–3.784), AJCC stage III (P<0.0001, HR = 2.111, 95%CI = 1.560–2.856), AJCC stage IV (P<0.0001, HR = 4.000, 95%CI = 3.023–5.293), TNM stage T4 (P = 0.0043, HR = 1.396, 95%CI = 1.111–1.756), TNM stage N3 (P = 0.0031, HR = 1.499, 95%CI = 1.146–1.961), lymph-node status (P = 0.0008, HR = 1.565, 95%CI = 1.206–2.032), surgery (P<0.0001, HR = 0.569, 95%CI = 0.445–0.726), and chemotherapy (P<0.0001, HR = 0.378, 95%CI = 0.323–0.441).

The SD model analysis showed that the independent prognostic factors were sex (P = 0.0043, HR = 0.793, 95%CI = 0.676–0.930), AJCC stage II (P<0.0001, HR = 2.455, 95%CI = 1.615–3.730), AJCC stage III (P = 0.0004, HR = 1.878, 95%CI = 1.324–2.663), AJCC stage IV (P<0.0001, HR = 3.495, 95%CI = 2.555–4.779), TNM stage T4 (P = 0.0362, HR = 1.334, 95%CI = 1.019–1.748), TNM stage N2 (P = 0.0131, HR = 1.403, 95%CI = 1.074–1.833), TNM stage N3 (P = 0.0395, HR = 1.395, 95%CI = 1.016–1.916), lymph-node status (P = 0.0012, HR = 1.626, 95%CI = 1.211–2.183), surgery (P = 0.0004, HR = 0.619, 95%CI = 0.474–0.808), and chemotherapy (P<0.0001, HR = 0.521, 95%CI = 0.427–0.636).

Finally, the CS model analysis showed that the independent prognostic factors were sex (P = 0.0012, HR = 0.789, 95%CI = 0.683–0.911), AJCC stage II (P<0.0001, HR = 2.905, 95%CI = 1.953–4.321), AJCC stage III (P<0.0001, HR = 2.217, 95%CI = 1.593–3.086), AJCC stage IV (P<0.0001, HR = 4.609, 95%CI = 3.390–6.265), TNM stage T2 (P = 0.0429, HR = 1.238, 95%CI = 1.007–1.522), TNM stage T4 (P = 0.001, HR = 1.518, 95%CI = 1.184–1.947), TNM stage N2 (P = 0.012, HR = 1.337, 95%CI = 1.066–1.677), TNM stage N3 (P = 0.0015, HR = 1.579, 95%CI = 1.190–2.093), lymph-node status (P = 0.0003, HR = 1.707, 95%CI = 1.275–2.285), surgery (P<0.0001, HR = 0.544, 95%CI = 0.418–0.709), and chemotherapy (P<0.0001, HR = 0.372, 95%CI = 0.314–0.440).

The results from the three types of model analysis demonstrate that sex, AJCC stage, TNM stage T4, TNM stage N3, lymph-node status, surgery, and chemotherapy are independent risk factors for the prognosis of pulmonary LCNEC (see Table 3).

Discussion

Pulmonary LCNEC is a rare primary malignant tumor with a poor prognosis. Clinical studies are urgently needed due to the current poor understanding of its biological behaviors, pathological features, and clinical effects. However, the Kaplan-Meier analysis and Cox proportional-hazards models used in most studies to detect independent prognostic factors may have limitations[7, 14].

Two widely used regression-based measurement methods have been used to analyze data affected by competing events: SD analysis and CS analysis[12, 13]. CS analysis reflects measures that are estimated when individuals exposed to competing event are censored, and adding SD analysis is worthwhile since it provides a complementary measure of risk: CS analysis might be more applicable for studying the etiology of diseases, whereas SD analysis might be more appropriate for predicting the risk that an individual has of a particular outcome[12, 15–17]. In addition, the sample size of competing events can exert different effects on the outcomes[7]. Traditional survival analysis methods might be subject to bias when the proportion of competing events is too high. The proportion of competing events was approximately 15% in the present study, and so we used a competing-risks model to screen for independent prognostic factors for pulmonary LCNEC.

We found that sex is an independent factor that influences the prognosis in both the univariate and multivariate analyses. The results of the three model analyses further indicated that being female was a protective factor for LCSD. A series of previous reports showed that pulmonary LCNEC patients are mainly male, elderly, and heavy smokers, with at least half of these patients having a history of smoking[18–21]. Similarly, recent analyses based on the SEER database have led to consistent conclusions[22–24]. In short, the survival outcomes of LCNEC differ between men and women, but confirming whether this difference is related to smoking requires further research.

The AJCC stage is still the most important and stable indicators for predicting the survival time of patients with lung cancer. The prognosis of these patients is strongly influenced by the stage of the tumor(s) at the time of their discovery. The prognosis of patients differs significantly between different clinical stages. All three of the current model analyses showed that the risk of death is highest for stage IV patients, but lower for stage III than for stage II. Given that a higher disease stage is generally associated with a worse prognosis, we considered that this contrasting finding may be related to the small number of stage II patients. Moreover, the three models also produced different results. The SD and CS models showed that TNM stage N2 was an independent prognostic factor, while the Cox model showed no such significant effect. Similarly, only the CS model showed that TNM stage T2 was an independent prognostic factor. Therefore, in the case where the associated directions are basically the same, the competitive risk model needs to provide the results of the CS model and SD model[17]. In addition, the HR values obtained from the different models were not consistent. Table 3 indicates that the effect size was smaller for the SD model than the Cox model, while it was largest for the CS model. Because we are mainly concerned with the prognostic factors for the disease, we are more inclined to accept the results of the SD model. However, the HR values for the Cox model were all larger than those for the SD model. This suggests that the Cox model ignores the risk of competition between outcomes and overestimates the outcome, and indicates that more caution is needed when interpreting the results of traditional survival analysis.

Given that we know very little about the clinicopathological and biological characteristics of pulmonary LCNEC, there is currently no uniform treatment available for reference. Previous studies have shown that surgery and examining the lymph nodes are very important for patients with early-stage pulmonary LCNEC[25–27]. However, the use of radiotherapy and chemotherapy remains controversial[28]. Our study showed that lymph-node status, surgery, and chemotherapy were independent prognostic factors. Patients in whom the lymph nodes were not examined or who had an unknown lymph-node status had a higher risk of death. Surgery and chemotherapy are protective factors for prognosis. Evidence is also accumulating that perioperative adjuvant chemotherapy is beneficial to the prognosis of patients with pulmonary LCNEC and is therefore a treatment option that should be considered[29–31]. However, the most suitable treatment method for patients at a particular disease stage needs to be explored in the future.

This study was subject to some limitations. First, since the study had a retrospective design, inherent bias might have been present. Second, since cases were only included if the required data were available, selection bias might have been present. Third, although the SEER database is a source of high-quality data for use in population-based studies, a considerable amount of treatment information remains unknown (e.g., about chemotherapy and the surgical sequence), which is not conducive to determining the treatment pattern applied to particular patients. Fourth, bias was also possible due to some of the samples in subgroups being too small.

Conclusion

We have conducted a competing-risks analysis of patients with pulmonary LCNEC based on the SEER database. The results show that sex, AJCC stage, TNM stage T4, TNM stage N3, lymph-node status, surgery, and chemotherapy are independent prognostic factors for pulmonary LCNEC patients. These data provide reference information that can be utilized for accurate assessments of the prognosis of this patient population.

Declarations

Ethics approval and consent to participate

Since all information in the SEER database has been de-identified, no institutional review board approval or informed consent was required for this study.

Consent for publication

All authors listed approved the publication of the manuscript

Conflicts of interest

The authors declare that there is no conflict of interest.

Funding source

None.

Author contributions

Qian Huang: data curation, formal analysis, writing original draft, and editing.

Jie Liu: methodology, supervision, review, and editing.

Qiao Huang: Data statistics, methodology, supervision, review, and editing.

Huifang Cai: supervision, review, and editing.

Qi Zhang: methodology, review, and editing.

Lina Wang: methodology, supervision, review, and editing.

Acknowledgments

None.

References

1. Fasano M, Della Corte CM, Papaccio F, Ciardiello F, Morgillo F. Pulmonary Large-Cell Neuroendocrine Carcinoma: From Epidemiology to Therapy. Journal of thoracic oncology: official publication of the International Association for the Study of Lung Cancer. 2015;10(8):1133–41. doi:10.1097/jto.0000000000000589.
2. Derks JL, Hendriks LE, Buikhuisen WA, Groen HJ, Thunnissen E, van Suylen RJ et al. Clinical features of large cell neuroendocrine carcinoma: a population-based overview. The European respiratory journal. 2016;47(2):615–24. doi:10.1183/13993003.00618–2015.
3. Travis WD, Linnoila RI, Tsokos MG, Hitchcock CL, Cutler GB, Jr., Nieman L et al. Neuroendocrine tumors of the lung with proposed criteria for large-cell neuroendocrine carcinoma. An ultrastructural, immunohistochemical, and flow cytometric study of 35 cases. The American journal of surgical pathology. 1991;15(6):529–53.
4. Varlotto JM, Medford-Davis LN, Recht A, Flickinger JC, Schaefer E, Zander DS et al. Should large cell neuroendocrine lung carcinoma be classified and treated as a small cell lung cancer or with other large cell carcinomas? Journal of thoracic oncology: official publication of the International Association for the Study of Lung Cancer. 2011;6(6):1050–8. doi:10.1097/JTO.0b013e318217b6f8.
5. Rekhtman N. Neuroendocrine tumors of the lung: an update. Archives of pathology & laboratory medicine. 2010;134(11):1628–38. doi:10.1043/2009–0583-rar.1.
6. Travis WD, Brambilla E, Nicholson AG, Yatabe Y, Austin JHM, Beasley MB et al. The 2015 World Health Organization Classification of Lung Tumors: Impact of Genetic, Clinical and Radiologic Advances Since the 2004 Classification. Journal of thoracic oncology: official publication of the International Association for the Study of Lung Cancer. 2015;10(9):1243–60. doi:10.1097/jto.0000000000000630.
7. Pintilie M. Dealing with competing risks: testing covariates and calculating sample size. Statistics in medicine. 2002;21(22):3317–24. doi:10.1002/sim.1271.
8. Provenzano M, Minutolo R, Chiodini P, Bellizzi V, Nappi F, Russo D et al. Competing-Risk Analysis of Death and End Stage Kidney Disease by Hyperkalaemia Status in Non-Dialysis Chronic Kidney Disease Patients Receiving Stable Nephrology Care. Journal of clinical medicine. 2018;7(12). doi:10.3390/jcm7120499.
9. Taylor SL, Sen S, Greenhalgh DG, Lawless M, Curri T, Palmieri TL. A competing risk analysis for hospital length of stay in patients with burns. JAMA surgery. 2015;150(5):450–6. doi:10.1001/jamasurg.2014.3490.
10. Dispinzieri M, La Rocca E, Meneghini E, Fiorentino A, Lozza L, Di Cosimo S et al. Discontinuation of hormone therapy for elderly breast cancer patients after hypofractionated whole-breast radiotherapy. Medical oncology (Northwood, London, England). 2018;35(7):107. doi:10.1007/s12032–018–1165–9.
11. Gray RJ. A Class of $K$-Sample Tests for Comparing the Cumulative Incidence of a Competing Risk. Ann Statist. 1988;16(3):1141–54. doi:10.1214/aos/1176350951.
12. Lau B, Cole SR, Gange SJ. Competing risk regression models for epidemiologic data. American journal of epidemiology. 2009;170(2):244–56. doi:10.1093/aje/kwp107.
13. Fine JP, Gray RJ. A Proportional Hazards Model for the Subdistribution of a Competing Risk. Journal of the American Statistical Association. 1999;94(446):496–509. doi:10.1080/01621459.1999.10474144.
14. Pepe MS, Mori M. Kaplan-Meier, marginal or conditional probability curves in summarizing competing risks failure time data? Statistics in medicine. 1993;12(8):737–51.
15. Koller MT, Raatz H, Steyerberg EW, Wolbers M. Competing risks and the clinical community: irrelevance or ignorance? Statistics in medicine. 2012;31(11–12):1089–97. doi:10.1002/sim.4384.
16. Wolbers M, Koller MT, Witteman JC, Steyerberg EW. Prognostic models with competing risks: methods and application to coronary risk prediction. Epidemiology (Cambridge, Mass). 2009;20(4):555–61. doi:10.1097/EDE.0b013e3181a39056.
17. Latouche A, Allignol A, Beyersmann J, Labopin M, Fine JP. A competing risks analysis should report results on all cause-specific hazards and cumulative incidence functions. Journal of clinical epidemiology. 2013;66(6):648–53. doi:10.1016/j.jclinepi.2012.09.017.
18. Asamura H, Kameya T, Matsuno Y, Noguchi M, Tada H, Ishikawa Y et al. Neuroendocrine neoplasms of the lung: a prognostic spectrum. Journal of clinical oncology: official journal of the American Society of Clinical Oncology. 2006;24(1):70–6. doi:10.1200/jco.2005.04.1202.
19. Takei H, Asamura H, Maeshima A, Suzuki K, Kondo H, Niki T et al. Large cell neuroendocrine carcinoma of the lung: a clinicopathologic study of eighty-seven cases. The Journal of thoracic and cardiovascular surgery. 2002;124(2):285–92.
20. Roesel C, Terjung S, Weinreich G, Gauler T, Theegarten D, Stamatis G et al. A Single-Institution Analysis of the Surgical Management of Pulmonary Large Cell Neuroendocrine Carcinomas. The Annals of thoracic surgery. 2016;101(5):1909–14. doi:10.1016/j.athoracsur.2015.12.009.
21. Paci M, Cavazza A, Annessi V, Putrino I, Ferrari G, De Franco S et al. Large cell neuroendocrine carcinoma of the lung: a 10-year clinicopathologic retrospective study. The Annals of thoracic surgery. 2004;77(4):1163–7. doi:10.1016/j.athoracsur.2003.09.070.
22. Cao L, Li ZW, Wang M, Zhang TT, Bao B, Liu YP. Clinicopathological characteristics, treatment and survival of pulmonary large cell neuroendocrine carcinoma: a SEER population-based study. PeerJ. 2019;7:e6539. doi:10.7717/peerj.6539.
23. Deng C, Wu SG, Tian Y. Lung Large Cell Neuroendocrine Carcinoma: An Analysis of Patients from the Surveillance, Epidemiology, and End-Results (SEER) Database. Medical science monitor: international medical journal of experimental and clinical research. 2019;25:3636–46. doi:10.12659/msm.914541.
24. Yang Q, Xu Z, Chen X, Zheng L, Yu Y, Zhao X et al. Clinicopathological characteristics and prognostic factors of pulmonary large cell neuroendocrine carcinoma: A large population-based analysis. Thoracic cancer. 2019;10(4):751–60. doi:10.1111/1759–7714.12993.
25. Eichhorn F, Dienemann H, Muley T, Warth A, Hoffmann H. Predictors of survival after operation among patients with large cell neuroendocrine carcinoma of the lung. The Annals of thoracic surgery. 2015;99(3):983–9. doi:10.1016/j.athoracsur.2014.10.015.
26. Sakurai H, Asamura H. Large-cell neuroendocrine carcinoma of the lung: surgical management. Thoracic surgery clinics. 2014;24(3):305–11. doi:10.1016/j.thorsurg.2014.05.001.
27. Kinoshita T, Yoshida J, Ishii G, Aokage K, Hishida T, Nagai K. The differences of biological behavior based on the clinicopathological data between resectable large-cell neuroendocrine carcinoma and small-cell lung carcinoma. Clinical lung cancer. 2013;14(5):535–40. doi:10.1016/j.cllc.2013.04.003.
28. Rieber J, Schmitt J, Warth A, Muley T, Kappes J, Eichhorn F et al. Outcome and prognostic factors of multimodal therapy for pulmonary large-cell neuroendocrine carcinomas. European journal of medical research. 2015;20:64. doi:10.1186/s40001–015–0158–9.
29. Tang H, Wang H, Xi S, He C, Chang Y, Wang Q et al. Perioperative chemotherapy with pemetrexed and cisplatin for pulmonary large-cell neuroendocrine carcinoma: a case report and literature review. OncoTargets and therapy. 2018;11:2557–63. doi:10.2147/ott.s160565.
30. Kim KW, Kim HK, Kim J, Shim YM, Ahn MJ, Choi YL. Outcomes of Curative-Intent Surgery and Adjuvant Treatment for Pulmonary Large Cell Neuroendocrine Carcinoma. World journal of surgery. 2017;41(7):1820–7. doi:10.1007/s00268–017–3908–8.
31. Iyoda A, Hiroshima K, Moriya Y, Iwadate Y, Takiguchi Y, Uno T et al. Postoperative recurrence and the role of adjuvant chemotherapy in patients with pulmonary large-cell neuroendocrine carcinoma. The Journal of thoracic and cardiovascular surgery. 2009;138(2):446–53. doi:10.1016/j.jtcvs.2008.12.037.

Tables

Table 1 Characteristics and demographics of patients with pulmonary large-cell neuroendocrine carcinoma.

Abbreviations: IQR, interquartile range; LCNEC, pulmonary large-cell neuroendocrine carcinoma; Lns, lymph nodes status; NOS, not otherwise specified.

Table 2 Univariate analysis of prognostic factors in patients with pulmonary large-cell neuroendocrine carcinoma.

Abbreviations: CIF, cumulative incidence function; Lns, lymph nodes status; NOS, not otherwise specified.

Table 3 Multivariate analysis of prognostic factors in patients with pulmonary large-cell neuroendocrine carcinoma.

Abbreviations: SD, subdistribution hazard function; CS, cause-specific hazard function; Lns, lymph nodes status; NOS, not otherwise specified; ref, reference; CI, confidence interval; HR, hazard ratio.