Effect and prediction of molecular subtype, metastatic site and treatment for early death in non-small cell lung cancer: A SEER-based study

doi:10.21203/rs.3.rs-3134535/v1

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Effect and prediction of molecular subtype, metastatic site and treatment for early death in non-small cell lung cancer: A SEER-based study

https://doi.org/10.21203/rs.3.rs-3134535/v1

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Background:

early death in patients with non-small cell lung cancer remains an unresolved issue. Therefore, it is necessary to identify the risk factors for early death in non-small cell lung cancer patients.

Purpose:

The purpose of this study was to identify associated risk factors and develop a predictive nomogram for the early death of non-small cell lung cancer patients.

Research method:

We enrolled 51529 patients in SEER Database who were 60 years or older, diagnosed with primary non-small cell lung cancer, from 2010 to 2016. All patients were randomly divided into training and testing sets at 7:3. Multivariate COX proportional hazard regression was used to identify independent risk variables for early death. Use K-M survival curve to compare different molecular subtypes, metastatic sites, and treatment methods on the survival status of lung cancer patients. A predictive nomogram was developed to predict the 1-year overall survival (OS) and then validated by calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA).

RESULTS:

Eight independent variables, including race, sex, age, tumor size, AJCC M, molecular subtype, metastatic site and treatment were recognized by using multivariate COX proportional hazard regression model for identifying independent risk variables of early death about non-small cell lung cancer patients. By comprising these variables, a predictive nomogram was constructed in the training set cohort. In the nomogram of 1-year overall survival, the areas under the ROC curves were 0.781 (95% CI: 0.771–0.804) and for the training dataset as well as (95% CI: 0.761–0.821) for the validation dataset. Then, the consistency between the predicted and actual overall survival was confirmed by the calibration curves. Additionally, the results of the DCA indicated that the predictive model had a favorable clinical applicability.

Conclusion:

In the early stages of non-small cell lung cancer (NSCLC), different molecular subtypes, metastatic sites, and treatment methods can significantly affect the survival rate of patients. The nomogram model developed in this study provides an insightful and applicable tool for predicting the risk of early mortality in NSCLC patients. It can help clinicians identify patients at high risk of early death and tailor their treatment plans accordingly, potentially improving patient outcomes.

Biological sciences/Cancer/Lung cancer

Biological sciences/Cancer/Lung cancer/Non small cell lung cancer

Non-small cell lung cancer

early death

SEER

In the past few decades, the death rate of lung cancer has remained high, which has greatly increased the social burden and endangered human health. Lung cancer still ranks second in the incidence of cancer spectrum in the world and ranks first in mortality. In 2021, the number of new cases reached 235,760 and the number of deaths will reach 131,800 in the United States[1], accounting for approximately 10% of the annual new cases of malignancies[2]. And it is mainly divided into non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC).

The early diagnosis of lung cancer lacks specificity, therefore, many patients are already in locally advanced NSCLC when they are discovered. As a result, distant metastasis of NSCLC has become a common clinical problem. Approximately 50% of NSCLC patients present with metastatic disease at diagnosis [3, 4], with the most common metastatic sites being the bone, followed by the lung, brain, liver, and adrenal gland. Meanwhile, it is critical to identify the molecular subtype of the tumor. Non-small cell lung cancer is divided into squamous cell carcinoma, adenocarcinoma, etc., accounting for approximately 80% of all lung cancers. In 2011, Yoshizawa et al. demonstrated a correlation between molecular subtypes and survival, suggesting some differences in survival outcomes between different lung cancer subtypes [5]. Therefore, molecular subtype is of great significance for the survival and prognosis of patients. With the continuous development of medical technology, in addition to radiotherapy, chemotherapy and other treatment methods, more and more advanced surgical methods are also used to treat lung cancer and improve the survival and prognosis of lung cancer patients. In recent years, the morbidity and mortality of lung cancer patients have decreased dramatically, and the survival rate has improved significantly, especially in patients with non-small cell lung cancer (NSCLC) [6, 7]. The estimated 2-year relative survival rate for NSCLC has improved from 34% (diagnosed in 2009 to 2010) to 42% (diagnosed in 2015 to 2016), including an absolute improvement of 5–6% in patients at all stages [8, 9]. Therefore, the treatment of lung cancer patients also has a certain impact on the survival of lung cancer patients.

Nomograms incorporating multiple clinical characteristics have been increasingly employed in recent years to forecast the prognosis of different types of lung cancer. These nomograms have been utilized in several studies and have demonstrated their usefulness in predicting outcomes for patients with lung cancer. [10–15] With the development of treatment methods, non-small cell lung cancer patients have opportunities to survive better. However, early death (survival time ≤ 1 year) of non-small cell lung cancer patients remains unsolved problems. Studies conducted in the past have explored various prognostic factors in patients with NSCLC, such as age, gender, type of treatment, tumor stage, and the number of examined lymph nodes, among others. (16–20). Therefore, it’s necessary to identify risk factors of early death in non-small cell lung cancer patients. Such studies have been reported in colorectal cancer, ovarian cancer, and gastric cancer, but the researches in non-small cell lung cancer were rarely reported [21, 22]. Therefore, this study aimed to recognize risk factors and establish a predictive nomogram for the early death of non-small cell lung cancer patients based on a large population cohort.

Ethics approval and consent to participate

Considering SEER database is publicly available and does not require informed patient consent. So, we did not need to get patient consent and exempt from Institutional Review Board approval. We signed a Data-Use Agreement for the SEER Research plus, 18 Registries (2000-2018) database File.

Data source and study population

We abstracted data from the SEER Research plus, 18 Registries (2000-2018) database and searched patients for non-small cell lung cancer diagnosed from 2010 to 2016. We enrolled 51528 patients according to the following inclusion criteria. Inclusion criteria: year of diagnosis from 2010 to 2016; age at diagnosis≥60 years; lung cancer as the only primary malignancy diagnosis; non-small cell carcinoma with morphology codes 8046, 8050–8052, 8070–8078, 8140–8147, 8250–8255, 8260, 8310, 8323, 8430, 8480, 8481, 8482, 8490, 8560, and 8570–8575. Patients of non-small cell lung cancer without histological confirmation, survival months, race, marital status, grade, and American Joint Committee on Cancer (AJCC) sixth edition stage were excluded. Demographic and clinical characteristics were extracted in the SEER database as follows: age at diagnosis, race, marriage, tumor stage, surgical method, histological classification, metastatic site, radiotherapy, chemotherapy, survival, AJCC, and survival time.

Outcome measurement

In our study, overall survival (OS) was used as a primary study outcome. Overall survival (OS) was defined as from the date of diagnosis to the date of death or was censored at the last follow-up date. Patients being lost to follow-up or survived at the end of the follow-up period were censored. If a patient was still alive at the end of the follow-up period, the follow-up duration was calculated from the date of diagnosis to the end of this study. If a patient was lost to follow-up, the follow-up duration was calculated from the date of diagnosis to the day of the last contact. This study aimed to investigate the risk factors for early death in non-small cell lung cancer patients. The 5-year survival rate of lung cancer is 90%, so we set the early death time of lung cancer as within 1 year.

Nomogram Development and Statistical analysis

Categorical data were described by the number and the percentage (N, %). Multivariate COX proportional hazard regression model was utilized to identify variables that significantly associated with the early death of non-small cell lung cancer in the SEER sets. Meanwhile, We use K-M survival curve to compare different histological subtypes, metastatic sites, and treatment methods affect the survival status of lung cancer patients. Then the predictive nomogram based on factors was constructed for the early death of non-small cell lung cancer by the training set cohort. To evaluate the calibration and discrimination of the nomogram, the test set was used. The receiver operating characteristic (ROC) curve was used to judge discrimination. The higher the area under the curve (AUC) was, the better the accuracy would be. Then, the consistency between the predicted and actual overall survival was confirmed by the calibration curves. Additionally, the results of the DCA indicated that the predictive model had a favorable clinical applicability. All analyses were performed using the R software (version 3.6.2), SPSS and stata (version 17.0). A two-tailed P value less than 0.001 was considered statistically significant.

Demographic and clinical characteristics

Through inclusion and exclusion criteria, a total of 51529 patients were ultimately included in the study. We randomly divided the patients into a training set and a testing set at 7:3. There were 36070 patients in the training set and 15459 patients in our test set enrolled in this study. The selection criteria for patients are shown in (Fig. 1).The demographic and clinical characteristics of non-small cell lung cancer were listed in Table 1. Regarding the training set, most patients (81.49%) were white people. 54.04% of the patients are adenocarcinoma and 35.31% are Squamous cell carcinoma. The most common metastatic sites were bone, accounting for approximately 8.59% of bone metastases. 46.10% of patients performed surgery and 57.45% of patients performed radiation. There were nearly 57.69% of patients that received chemotherapy.

Since the random allocation was conducted, the p-value between the training and testing datasets was less than 0.001, indicating that there was a very small difference between the data from the two sets. Therefore, there is minimal difference between the baseline information of the data.

Table 1. Patient baseline information

Patient baseline information						P value
Value		trainning set		test set
Sex	female	17110	47.44%	7183	47.39%	<.001
Sex	male	18958	52.56%	8000	52.78%
Race	white	29393	81.49%	12682	83.67%	<.001
	black	3375	9.36%	2034	13.42%
	other	3300	9.15%	743	4.90%
Age	60-79	22835	63.31%	9484	62.57%	<.001
Age	80+	13233	36.69%	5975	39.42%	<.001
Marital status	no	15991	44.33%	7515	49.58%	<.001
Marital status	yes	20077	55.66%	7944	52.41%	<.001
tumor size	<3cm	10131	28.09%	2825	18.64%	.006
	<7cm	12343	34.22%	4439	29.28%
	>7cm	13595	37.69%	8194	54.06%
Mets at bone	no	32969	91.41%	12565	82.89%	.000
Mets at bone	yes	3099	8.59%	2894	19.09%	.000
Mets at brain	no	33804	93.72%	13499	89.06%	.000
Mets at brain	yes	2264	6.28%	1960	12.93%	.000
Mets at liver	no	34844	96.60%	14097	93.00%	.000
Mets at liver	yes	1224	3.39%	1362	8.99%	.000
AJCC M, 7th	M0	26140	72.47%	7835	51.69%	.000
	M1a	3409	9.45%	2114	13.95%	.000
	M1b	6519	18.07%	5510	36.35%
AJCC N, 7th	N0	19809	54.92%	6294	41.52%	<.001
	N1	3705	10.27%	1486	9.80%
	N2	9748	27.03%	5798	38.25%
	N3	2806	7.78%	1881	12.41%
AJCC T, 7th	T1	10130	28.09%	2826	18.64%	<.001
	T2	12343	34.22%	4439	29.28%
	T3	7289	20.21%	3900	25.73%
	T4	6306	17.48%	4294	28.33%
Grade	I	4225	11.71%	1156	7.63%	<.001
	II	13796	38.25%	4903	32.35%
	III	17360	48.13%	9032	59.59%
	Ⅳ	687	1.90%	368	2.43%
Histologic Type	adenocarcinoma	19493	54.04%	7347	48.47%	<.001
	Squamous cell carcinoma	12737	35.31%	5757	37.98%
	Large cell carcinoma	631	1.75%	369	2.43%
	other	3207	8.89%	1986	13.10%
Surg Prim Site	no	19441	53.90%	11337	74.79%	<.001
Surg Prim Site	yes	16627	46.10%	4122	27.19%
Radiation	no	12844	42.55%	7590	50.07%
Radiation	yes	16583	57.45%	7577	49.99%	<.001
Chemotherapy	no	12987	42.31%	8120	43.57%	<.001
Chemotherapy	yes	16431	57.69%	8706	57.44%	<.001

Identifying independent factors for early death

The cox proportional hazards regression model was used to statistically analyze the baseline information of the patients, and we calculated the risk of patient's 1-year and 5-years mortality status based on different baseline information. The results are shown in Table 2. Among them, we can find that regardless of early-stage patients or advanced-stage patients, the differences in tumor size, treatment methods, cancer metastasis, and AJCC M staging have a significant impact on the survival of patients. Patients with a tumor diameter of 7 cm or more have a significantly higher risk of death than patients with a tumor diameter of less than 7 cm; in terms of treatment, the risk of death for patients who have undergone surgery, chemotherapy, and radiotherapy is greatly reduced. Patients with metastatic cancer also had a significantly increased risk of death. Secondly, in patients with late death, the impact of age, gender, race, and grade differentiation on the survival status of patients is more obvious than that in patients with early death. In the early death patients, the P value of the cox regression analysis of Grade and AJCC N stage was too high, which was not statistically significant, so it basically had no effect on the death of the patients. Tumor histomorphology, age, and marital status had a certain degree of influence on the death of patients.

By applying multivariate COX proportional hazard regression in the SEER cohort, the risk factors associated with early death of non-small cell lung cancer were analyzed (Table 2). multivariate COX proportional hazard regression models showed that age, race, sex, tumor size, AJCC M stage, metastases site, therapy, molecular subtype were associated with early death.

Table 2. Multivariate Cox proportional hazards regression for early and late death in patients with non-small cell lung cancer

	Multifactorial cox proportional-hazards model within 5 years				Multifactorial cox proportional-hazards model within 1 year
		95%CI				95%CI
Variables	OR	lower limit	upper limit	p vaule	OR	Lower limit	upper limit	p vaule
age(years)
60-79	Ref				Ref
80+	1.235	1.209	1.261	<0.001	1.084	1.055	1.114	<0.001
Sex
Felmale	Ref				Ref
Male	1.267	1.241	1.294	<0.001	1.086	1.052	1.144	<0.001
Race
white	Ref				Ref
black	0.941	0.911	0.972	<0.001	0.959	0.920	0.999	0.045
other	0.782	0.752	0.813	<0.001	0.901	0.855	0.950	<0.001
marital status
no	Ref				Ref
yes	0.892	0.874	0.911	<0.001	0.912	0.864	0.952	<0.001
tumor size
<3cm	Ref				Ref
<7cm	1.470	1.425	1.515	<0.001	1.320	1.287	1.455	<0.001
>7cm	1.878	1.819	1.938	<0.001	1.673	1.535	1.761	<0.001
Mets at bone
no	Ref				Ref
yes	1.211	1.167	1.256	<0.001	1.180	1.133	1.230	<0.001
Mets at brain
no	Ref				Ref
yes	1.406	1.350	1.464	<0.001	1.292	1.235	1.353	<0.001
Mets at liver
no	Ref				Ref
yes	1.262	1.207	1.319	<0.001	1.161	1.107	1.218	<0.001
AJCC M,7th
M0	Ref				Ref
M1a	1.585	1.527	1.646	<0.001	1.288	1.236	1.342	<0.001
M1b	1.797	1.729	1.868	<0.001	1.294	1.238	1.353	<0.001
AJCC N,7th
N0	Ref				Ref
N1	1.491	1.439	1.546	<0.001	1.072	1.022	1.125	0.005
N2	1.627	1.583	1.671	<0.001	1.159	1.122	1.197	<0.001
N3	1.615	1.555	1.677	<0.001	1.125	1.077	1.176	<0.001
Grade
I	Ref				Ref
II	1.286	1.233	1.340	<0.001	0.986	0.926	1.050	0.665
III	1.466	1.406	1.528	<0.001	1.079	1.014	1.147	0.016
Ⅳ	1.498	1.379	1.628	<0.001	1.138	1.026	1.263	0.015
Histologic Type
adenocarcinoma	Ref				Ref
Squamous cell carcinoma	1.213	1.185	1.241	<0.001	1.083	1.050	1.116	<0.001
Large cell carcinoma	1.355	1.255	1.462	<0.001	1.051	0.960	1.151	0.281
other	1.292	1.249	1.337	<0.001	1.150	1.104	1.199	<0.001
Radiation
no	Ref				Ref
yes	0.814	0.795	0.834	<0.001	0.777	0.754	0.800	<0.001
Chemotherapy
no	Ref				Ref
yes	0.537	0.525	0.550	<0.001	0.505	0.491	0.520	<0.001
Surg Prim Site
no	Ref				Ref
yes	0.345	0.334	0.356	<0.001	0.782	0.750	0.816	<0.001

KM survival curves

KM survival curves were used to analyze the influence of different tumor morphology, metastatic sites and different treatment methods on the survival status of patients. Figure 2a shows the KM analysis of patients with different histological subtypes, where 1, 2, 3, and 4 represent adenocarcinoma, squamous cell carcinoma, large cell carcinoma, and other types of non-small cell lung cancer, respectively. Through the K-M survival curve, it can be found that the cure rate of squamous cell carcinoma is the highest. The risk of death from large cell carcinoma and other types of non-small cell carcinoma was significantly lower in the early stage of cancer than in patients with squamous cell carcinoma and adenocarcinoma. In addition to patients with adenocarcinoma, the risk of death in patients with other non-small cell carcinomas was basically the same.

Figure 2b displays the KM analysis of non-small cell lung cancer patients with different metastatic conditions, where 0 represents no metastasis, and 1, 2, 3 represent bone metastasis, brain metastasis, and liver metastasis respectively. 4 represents multiple metastases. We can find that when the cancer metastasizes, the patient's risk of death increases significantly. Among them, the average survival month of non-small cell carcinoma patients without metastasis was 23 months, and the average survival month of patients with cancer metastasis was 6 months. And in the early stage of cancer, the cure rate of patients with bone metastases and brain metastases is slightly higher than that of liver metastases, and patients with multiple metastases have the highest risk of death. The risk of death in patients with metastases in the late stage is extremely high, almost close to it.

Figure 2c demonstrates the KM analysis of patients receiving different treatment methods, where 0 represents no treatment, and 1, 2, 3 represent patients receiving surgery, radiotherapy, and chemotherapy, respectively. Through the K-M survival curve, we can find that surgery at the primary site of cancer significantly improves patient survival. The survival rate of patients can also be improved by chemotherapy or radiotherapy, and the curative effect of the two is basically the same in the early stage. Over time, in the treatment of advanced patients, the effect of chemotherapy is slightly higher than that of radiotherapy patients, and radiotherapy is of little significance to the treatment of patients at this time.

Nomogram construction

Nomogram are often used to build clinical prediction models. We used clinical data from the training set of non-small cell lung cancer patients to construct a nomogram for assessing and predicting the risk of early death in patients (figure 3). First, we identified risk factors for mortality through Cox proportional hazard regression and K-M survival curve analysis. We found that tumor size, treatment method, AJCC staging, and cancer metastasis had a significant impact on the early death of non-small cell lung cancer patients. Second, age, race, gender, and tumor subtype also affected patient mortality. Therefore, we selected these factors and performed Cox proportional hazard regression analysis using R language to construct a column chart for predicting the risk of early death in non-small cell lung cancer patients, which could help doctors and patients better predict the probability of death in cancer patients.

Nomogram validation

The predictive nomogram was validated both in the training set and the test set. For the test set, nomogram predicted probabilities of early death were computed according to the nomogram based on the train set. Figure 4 shows the ROC curves, where 4a and 4c represent the ROC curves for predicting 1-year non-small cell lung cancer death in the training and test sets, respectively, while 4b and 4d represent the ROC curves for predicting 5-year death in the training and test sets, respectively. The ROC curve areas of the line graphs for predicting death within 1 year and 5 years in the training set of non-small cell lung cancer were 0.781 (95% CI: 0.771–0.804) and 0.740 (95% CI: 0.721–0.732), respectively. In the test set, the ROC curve areas of the line graphs for predicting death within 1 year and 5 years were 0.77(95% CI: 0.761–0.821) and 0.727(95% CI: 0.702–0.845), respectively. This also demonstrates that our model is better suited for predicting early death in non-small cell lung cancer. Figure 5 shows the calibration curves of the model under different conditions. The calibration plots performed well both in the training and validation cohort. Besides, figure 6 shows the Decision Curve Analysis curves of the model, the results demonstrate that the model has good clinical value.

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, accounting for 85% of lung cancer patients. Due to the lack of clear early diagnosis methods, most patients are diagnosed with lung cancer at a late stage, resulting in a very low early survival rate for lung cancer patients. However, previous studies have focused only on long-term survival of patients, and rarely on early death. Therefore, through our research, we found that the main factors related to early death in NSCLC patients include histological subtype, treatment method, and site of metastasis.

Firstly, we found that different histological subtypes affect the early death of NSCLC patients. NSCLC mainly includes squamous cell carcinoma, adenocarcinoma, large cell carcinoma, and other types. Different molecular subtypes of cancer patients have certain differences in cancer metastasis. Through analysis, we should pay special attention to patients with large cell carcinoma and intervene in a timely manner to reduce the risk of metastasis and death.

Secondly, different treatment methods significantly affect the survival prognosis of patients. Surgery at the primary site of cancer significantly improves the cure rate of patients. We also found that the efficacy of chemotherapy and radiotherapy varies for patients at different stages. Therefore, we can provide some reference for the treatment methods of patients.

Thirdly, the prognosis of patients with metastatic NSCLC is poor, and there are differences in the prognosis among different metastatic sites. In our study, about 20% of NSCLC patients will experience metastasis. Lung cancer has some preferred sites of metastasis, such as the brain, bones, and liver. In the late stage of the disease, it usually affects other organs. There are also preferred sites of metastasis in different types of lung cancer. Therefore, we should pay special attention to cancer metastasis patients and evaluate their risk of death based on different metastatic sites.

Nomograms had been widely developed and used for predicting long-term survival of cancers [23-27]. To the best of our knowledge, this study is the first to identify the risk factors associated with early death and structure an available nomogram for predicting early death in patients with non-small cell lung cancer. For the SEER cohort, multivariate analyses identified eight independent risk factors, including age, race, sex, tumor size, AJCC M stage, metastases site, therapy, molecular subtype. Some previous studies had reported that treatment, molecular subtype, and distant metastasis were significantly related to overall survival [28, 29]. This study firstly confirmed that these risk factors were related to early death. A convenient nomogram containing identified independent factors was constructed for predicting early death of non-small cell lung cancer patients. Once they were distinguished, closer attention and more effective treatment strategies such as clinical trials might be given by oncologists.

There were several limitations to this study. First the excluded missing data might result in selection bias. Secondly, there may be limitations in generalizing the nomogram to Asian populations given the use of data from the SEER database in the United States; Thirdly, though a test set was used to validate the nomogram, the article did not conduct external verification on the nomogram, a large prospective study was requisite.

Through the statistical analysis of clinical data of patients with non-small cell carcinoma, the study investigates the impact of tumor histological morphology, treatment methods, and metastasis status on the risk of death in patients with non-small cell carcinoma. Finally, the nomogram was constructed based on the patient's baseline information to help doctors and patients predict the risk of early death of patients. All in all, a comprehensive nomogram integrating identified risk factors and a nomogram were constructed to distinguish the early death of non-small cell lung cancer patients. The nomogram might help oncologists to make better therapeutic strategies such as clinical trials, and hospice management.

Contributions

The authors acknowledge the Surveillance, Epidemiology, and End Results (SEER) Program tumor registries for providing the data source for this study. The acquisition, analysis, and interpretation of these data are solely the responsibility of the authors. RY and JH oversaw the writing and revision of the manuscript. All authors have read and approved the final manuscript.

Ethics declarations

Ethics approval and consent to participate

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

For this type of study formal consent is not required.

Competing interests

The authors certify that there is no confict of interest regarding this manuscript.

Acknowledgments

This study was supported by Yibin Science and Technology Program of 2022 (No.2022ZYD06) and Sichan Province Major Development Program of 2022 (No.22ZDYF0376).The authors would like to express their gratitude to the tumor registries of the Surveillance, Epidemiology, and End Results (SEER) Program for their contributions to the creation of the SEER database. However, it should be noted that the interpretation and reporting of the data presented in this study are the sole responsibility of the authors. Additionally, the authors would like to extend their appreciation to the SEER database for providing a valuable dataset, as well as to their colleagues in the department for their support throughout the study.

data availability

All data generated or analysed during this study are included in this published article [and its supplementary information files].

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Effect and prediction of molecular subtype, metastatic site and treatment for early death in non-small cell lung cancer: A SEER-based study

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