Genetic variants of NEUROD1 target genes are associated with clinical outcomes of small‐cell lung cancer patients

Abstract

Background

Neurogenic differentiation factor 1 (NEUROD1) is frequently overexpressed in small‐cell lung cancer (SCLC). NEUROD1 plays an important role in promoting malignant behavior and survival.

Methods

In this study, we evaluated the association between putative functional polymorphisms in 45 NEUROD1 target genes and chemotherapy response and survival outcomes in 261 patients with SCLC. Among the 100 single nucleotide polymorphisms (SNPs) studied, two were significantly associated with both chemotherapy response and overall survival (OS) of patients with SCLC.

Results

The SNP rs3806915C>A in semaphorin 6A (SEMA6A) gene was significantly associated with better chemotherapy response and OS (p = 0.04 and p = 0.04, respectively). The SNP rs11265375C>T in nescient helix–loop helix 1 (NHLH1) gene was also associated with better chemotherapy response and OS (p = 0.04 and p = 0.02, respectively). Luciferase assay showed a significantly higher promoter activity of SEMA6A with the rs3806915 A allele than C allele in H446 lung cancer cells (p = 4 × 10⁻⁶). The promoter activity of NHLH1 showed a significantly higher with the rs11265375 T allele than C allele (p = 0.001).

Conclusion

These results suggest that SEMA6A rs3806915C>A and NHLH1 rs11265375C>T polymorphisms affect the promoter activity and expression of the genes, which may affect the survival outcome of patients with SCLC.

Association between rs3806915C>A and rs11265375C>T and the response to chemotherapy and overall survival.

INTRODUCTION

Lung cancer remains the leading cause of cancer‐related deaths worldwide. In 2020, more than 2.2 million new cases and 1.8 million deaths because of lung cancer were recorded. ¹ Lung cancer is largely divided into non–small‐cell lung cancer (NSCLC) and small‐cell lung cancer (SCLC), which account for ~85% and 15% of all cases, respectively. In the past 10 years, the 5‐year survival rate of lung cancer has increased from 16% to 21% owing to innovations in cancer treatment such as immunotherapy and targeted anticancer drugs. ¹ , ² However, these novel therapeutics have shown significant beneficial effects in NSCLC, but not in SCLC. The 2‐year survival rate for extensive disease (ED) SCLC, which accounts for approximately 70% of SCLC cases, is only 8%. ¹ Although the recent addition of immune checkpoint inhibitors (ICIs) to cytotoxic chemotherapy has improved the overall survival (OS) in ED‐SCLC, ³ , ⁴ this is still marginal compared to the significant breakthroughs in the treatment of NSCLC. Therefore, there is a need to identify predictive biomarkers or develop new therapeutics to improve the survival outcomes of SCLC.

SCLC is a highly aggressive pulmonary neuroendocrine tumor characterized by rapid tumor growth, high vascularity, genomic instability, and early metastasis compared with NSCLC. ⁵ Our understanding of the biology and genomic alterations in SCLC has broadened over the past decade. The majority of SCLCs are characterized by inactivation of TP53 and RB1 tumor suppressor genes. ⁶ It was also known that MYC amplification, commonly found in SCLC, is related to short survival time. ⁷ With the advancements in cancer genetics, efforts are being made to further classify SCLCs from two subtypes (variant and classical) based on gene expression profiles. Many researchers have classified SCLC based on gene expression of achaete‐scute homologue 1 (ASCL1) and neurogenic differentiation factor 1 (NEUROD1) ⁸ , ⁹ , ¹⁰ , ¹¹ into three types as: ASCL1‐high, NEUROD1‐high, and double negative, or into four types by further dividing the double negatives. ⁸ , ⁹ , ¹⁰ , ¹¹

The basic helix–loop–helix transcription factors ASCL1 and NEUROD1 play important roles in promoting malignant behavior and survival of SCLC. ⁸ ASCL1 is essential for neuroendocrine differentiation in the lungs and plays a crucial role in SCLC carcinogenesis. ¹² , ¹³ ASCL1 is expressed in ~75% of SCLCs and functions as a lineage‐specific oncogene. ⁶ , ¹⁴ NEUROD1 is also critical for promoting neuronal differentiation and maturation. ¹⁵ , ¹⁶ NEUROD1 is expressed in ~15% of SCLCs and is associated with the variant subtype. ⁶ , ¹⁰ NEUROD1 is thought to promote tumor cell migration and therefore, contribute to metastasis in SCLC. ¹⁷ Its role as a regulatory hub in SCLC, through signaling molecules such as tyrosine kinase tropomyosin‐related kinase B and neural cell adhesion molecule, has been reported. ¹⁸ Therefore, ASCL1, NEUROD1, and their target genes are potential therapeutic targets for SCLC. ⁵ , ¹⁴ , ¹⁸

In a previous study, we found that a polymorphism in dopa decarboxylase, an ASCL1 target gene, was associated with survival outcomes in patients with SCLC. ¹⁹ We hypothesized that functional polymorphisms in NEUROD1 target genes may also affect the clinical outcomes of patients with SCLC, as NEUROD1 plays a crucial role in SCLC carcinogenesis. To test this hypothesis, we evaluated the association between putative functional polymorphisms in 45 NEUROD1 target genes and the chemotherapy response and survival outcomes of patients with SCLC.

RESULTS

Patient characteristics

The baseline characteristics of the 261 patients are presented in Table 1. The response rate to first line chemotherapy was 72.8% (95% confidence interval [CI], 67.4–78.2) and was higher with irinotecan‐cisplatin (IP) regimen than with etoposide‐cisplatin (EP) regimen (78.7% vs. 67.2%, p = 0.04). However, the OS did not differ between the regimens. The median survival time was 10.5 months (95% CI, 9.3–11.4). Younger age, limited‐stage disease, good performance status, low neuron‐specific enolase level, no weight loss, receiving second line chemotherapy, and radiation to the tumor were associated with better OS (Table 1). These variables were adjusted in subsequent studies to determine their association with the polymorphisms.

TABLE 1.

Univariate analysis for response to chemotherapy and overall survival by clinical variables.

Variables	No. of cases	Response to chemotherapy				Overall survival
		Responders (CR + PR) a	Non‐responders (SD + PD) a	OR (95% CI)	p	MST (months)	95% CI	Log‐rank p	HR (95% CI)	p
Overall	261	190 (72.8)	71 (27.2)	b		10.5	9.3–11.4
Age (years)
<68	129	100 (77.5)	29 (22.5)	1.00		11.8	11.0–13.5		1.00
≥68	132	90 (68.2)	42 (31.8)	0.62 (0.36–1.08)	0.09	7.9	7.1–9.4	3 × 10−4	1.63 (1.25–2.13)	3 × 10−4
Gender
Male	226	163 (72.1)	63 (27.9)	1.00		10.5	9.2–11.4		1.00
Female	35	27 (77.1)	8 (22.9)	1.30 (0.56–3.02)	0.54	11.0	6.4–15.3	0.75	0.94 (0.63–1.40)	0.75
Smoking status
Never	19	16 (84.2)	3 (15.8)	1.00		11.4	6.4–15.5		1.00
Ever	242	174 (71.9)	68 (28.1)	0.48 (0.14–1.70)	0.26	10.3	9.2–11.3	0.82	1.06 (0.64–1.76)	0.82
Clinical stage
LD	66	46 (69.7)	20 (30.3)	1.00		13.0	10.7–15.5		1.00
ED	195	144 (73.8)	51 (26.2)	1.23 (0.66–2.27)	0.51	9.6	8.2–10.9	0.001	1.67 (1.21–2.30)	2 × 10−3
ECOG
0–1	215	162 (75.4)	53 (24.6)	1.00		10.9	10.0–11.7		1.00
2	46	28 (60.9)	18 (39.1)	0.51 (0.26–0.99)	0.05	7.2	4.3–9.2	3 × 10−4	1.82 (1.31–2.53)	4 × 10−4
NSE
<14.7	96	66 (68.8)	30 (31.2)	1		11.4	10.2–14.0		1.00
≥14.7	147	109 (74.2)	38 (25.8)	1.30 (0.74–2.30)	0.36	9.4	7.7–10.5	0.02	1.41 (1.07–1.87)	0.02
Weight loss
No	185	139 (75.1)	46 (24.9)	1.00		11.2	10.2–12.2		1.00
Yes	76	51 (67.1)	25 (32.9)	0.68 (0.38–1.21)	0.19	8.1	7.2–10.2	0.01	1.42 (1.07–1.89)	0.01
Chemotherapy regimen
EP	134	90 (67.2)	44 (32.8)	1.00		10.9	9.0–12.4		1.00
IP	127	100 (78.7)	27 (21.3)	1.81 (1.04–3.16)	0.04	10.2	8.9–11.4	0.88	0.98 (0.75–1.28)	0.88
2nd line chemotherapy
Yes	140					12.2	11.2–13.7		1.00
No	121					7.2	6.2–8.4	1 × 10−5	1.80 (1.38–2.35)	1 × 10−5
Radiation to tumor
Yes	34					16.7	13.0–30.8		1.00
No	227					9.6	8.2–10.8	1 × 10−5	3.02 (1.78–5.10)	1 × 10−5

Abbreviations: CI, confidence interval; CR, complete response; ECOG, Eastern Cooperative Oncology Group; EP, etoposide/cisplatin; HR, hazard ratio; IP, irinotecan/cisplatin; MST, median survival time; NSE, neuron‐specific enolase; OR, odds ratio; PD, progressive disease; PR, partial response; PS, performance status; SD, stable disease.

^a Row percentage.

^b Responders 95% CI, 67.4–78.2, non‐responders 95% CI, 21.8–32.6.

Association between single nucleotide polymorphisms and treatment outcomes

Among the 100 single nucleotide polymorphisms (SNPs) evaluated, two showed significant association with both chemotherapy response and OS. SEMA6A rs3806915C>A was significantly associated with better chemotherapy response and OS (under a codominant model, adjusted odds ratio [aOR], 1.74; 95% CI, 1.02–2.95; p = 0.04, and aHR, 0.78; 95% CI, 0.62–0.99; p = 0.04, respectively) (Table 2 and Figure 1). NHLH1 rs11265375C>T was also significantly associated with better chemotherapy response and OS (under a dominant model, aOR, 1.95; 95% CI, 1.04–3.65; p = 0.04, and aHR, 0.70; 95% CI, 0.52–0.95; p = 0.02, respectively) (Table 2 and Figure 1).

TABLE 2.

The association between SEMA6A rs3806915C>A and NHLH1 rs11265375C>T and the response to chemotherapy and overall survival.

Gene polymorphism	Genotype	Responders (%) a	Non‐responders (%) a	OR (95% CI) b	p b	No. of cases (%) c	L‐R‐P	HR (95% CI) d	p d
SEMA6A rs3806915C>A	CC	116 (70.3)	49 (29.7)	1.00		165 (64.0)	0.11	1.00
	CA	58 (74.4)	20 (25.6)	1.34 (0.70–2.57)	0.38	78 (30.2)		0.87 (0.63–1.19)	0.37
	AA	14 (93.3)	2 (6.7)	7.77 (0.97–62.55)	0.05	15 (5.8)		0.50 (0.25–0.97)	0.04
	Dominant			1.63 (0.87–3.07)	0.13		0.10	0.78 (0.58–1.05)	0.11
	Recessive			7.03 (0.88–55.88)	0.07		0.07	0.52 (0.27–1.00)	0.05
	Codominant			1.74 (1.02–2.95)	0.04			0.78 (0.62–0.99)	0.04
NHLH1 rs11265375C>T	CC	98 (66.2)	50 (33.8)	1.00		148 (58.1)	0.22	1.00
	CT	70 (82.4)	15 (17.6)	2.16 (1.08–4.32)	0.03	85 (33.3)		0.62 (0.45–0.87)	0.005
	TT	17 (77.3)	5 (22.73)	1.34 (0.44–4.12)	0.61	22 (8.6)		1.11 (0.66–1.87)	0.70
	Dominant			1.95 (1.04–3.65)	0.04		0.09	0.70 (0.52–0.95)	0.02
	Recessive			1.06 (0.35–3.20)	0.92		0.90	1.28 (0.76–2.15)	0.35
	Codominant			1.51 (0.92–2.48)	0.10			0.84 (0.66–1.07)	0.16

Abbreviations: CI, confidence interval; HR, hazard ratio; L‐R‐P, log‐rank P; OR, odds ratio.

^a Row percentage.

^b ORs, 95% CI, and their corresponding p values were calculated using multivariate regression analysis, adjusted for age, sex, smoking status, stage, Eastern Cooperative Oncology Group performance status, weight loss, chemotherapy regimen, and neuron‐specific enolase.

^c Column percentage.

^d HRs, 95% CI and their corresponding p values were calculated using multivariate Cox proportional hazard models, adjusted for age, sex, smoking status, stage, Eastern Cooperative Oncology Group performance status, weight loss, chemotherapy regimen, 2nd line chemotherapy, radiotherapy, and neuron‐specific enolase.

FIGURE 1.

Kaplan–Meier curves for overall survival according to polymorphisms (a) SEMA6A rs3806915C>A and (b) NHLH1 rs11265375C>T. p values were calculated using multivariate Cox proportional hazard models (rs3806915C>A under a codominant model and rs11265375C>T under a dominant model).

Effect of SNPs on the promoter activity of SEMA6A and NHLH1

The SNP rs3806915C>A is located in the SEMA6A promoter region (−1621 base pairs [bp] from the transcription start site). We performed a luciferase assay to assess the effect of rs3806915C>A on SEMA6A promoter activity. Promoter activity was significantly higher for the rs3806915 A allele than for the rs3806915 C allele in H446 lung cancer cells (p = 4 × 10⁻⁶) (Figure 2).

FIGURE 2.

Relative luciferase activity according to polymorphisms. The effect of (a) SEMA6A rs3806915C>A and (b) NHLH1 rs11265375C>T genotypes on the promoter activity of the respective gene in H446 lung cancer cells. Data are presented as mean ± standard error of mean. p values are based on a t‐test.

SNP rs11265375C>A is located in the first intron of the NHLH1 gene. However, based on the high chromatin accessibility (as measured by DNase I hypersensitivity) ²⁰ and strong signal for active histone markers (H3K4Me3 and H3K27Ac) ²¹ at the chromosomal position of rs11265375C>A in the University of California Santa Cruz (UCSC) genome browser, rs11265375C>A was predicted to affect promoter activity (Figure 3). The luciferase assay also showed a significantly higher promoter activity for the rs11265375 T allele than for the rs11265375 C allele in H446 lung cancer cells (p = 0.001) (Figure 2).

FIGURE 3.

Bioinformatics annotation of NHLH1 promoter region using the University of California Santa Cruz (UCSC) genome browser. (a) UCSC genome browser view of chromosome 1q23.2 with data from the transcription factor ChIP‐seq, DNase 1 hypersensitivity, histone modifications from the ENCODE project. The histone modification tracks show the level of enrichment of the histone marks across the genome as determined by a ChIP‐seq assay using the seven cell lines of the ENCODE project. The next track shows DNase I hypersensitivity clusters. The last track represents transcription factor ChIP‐seq clusters (338 factors from 130 cell types) from ENCODE 3. The gray box encloses each peak cluster of transcription factor occupancy: the darkness of the box is proportional to the maximum signal strength observed in any cell type contributing to the cluster. (b) Definitions of track colors are listed.

DISCUSSION

In this study, we investigated the association between genetic variants of NEUROD1 target genes and the clinical outcomes of patients with SCLC. We found that two SNPs, SEMA6A rs3806915C>A and NHLH1 rs11265375C>T, were significantly associated with both chemotherapy response and OS in patients with SCLC. Additionally, we found that the promoter activity of each gene was significantly higher in the variant allele than in the wild‐type allele in in vitro functional studies.

SEMA6A is a member of the semaphorin family, which is known to regulate cell motility and attachment during axon guidance, vascular growth, immune cell regulation, and tumor progression. ²² SEMA6A has been proposed to be a prognostic biomarker that reduces cancer cell proliferation, migration, and invasion in glioblastoma. ²³ Recently, a few studies have analyzed the role of SEMA6A in lung cancer. ²⁴ , ²⁵ Chen et al. ²⁴ reported that overexpression of SEMA6A decreases lung cancer cell migration and suggested the role of SEMA6A in inhibition of cancer cell migration. Shen et al. ²⁵ showed that overexpression of SEMA6A reduces the proliferation of lung cancer cells and increases the rate of apoptosis. As rs3806915C>A is located in the SEMA6A promoter region (−1621 bp from the transcription start site), it may alter the promoter activity of SEMA6A. Results of the luciferase assay revealed that the promoter activity of SEMA6A was higher for the rs3806915 A allele than for the C allele. Furthermore, we found that SEMA6A rs3806915C>A was significantly associated with better chemotherapy response and OS. This is consistent with the results of the aforementioned studies. SEMA6A rs3806915C>A increases the promoter activity of SEMA6A, thereby increasing the expression of SEMA6A, which in turn reduces lung cancer cell migration and proliferation and increases apoptosis, leading to better OS. Dhanabal et al. ²⁶ reported that recombinant SEMA6A‐1 soluble extracellular domain inhibits growth factor and tumor‐induced angiogenesis in vivo, suggesting the potential therapeutic role of SEMA6A. Therefore, SEMA6A represents an attractive therapeutic target for treating lung cancer.

NHLH1, also known as HEN1 and NSCL1, encodes helix–loop–helix protein 1, which plays a role in the growth and development of a wide variety of tissues, particularly in regulating neurogenesis. ²⁷ , ²⁸ Misexpression of NSCL1 leads to abnormal brain development in chicks. ²⁹ NHLH1 has been reported to be associated with neuroblastoma and medulloblastoma. ³⁰ , ³¹ However, its association with other cancers is unknown. In this study, NHLH1 rs11265375C>T was found to be significantly associated with chemotherapy and OS in patients with SCLC. NHLH1 rs11265375C>T is located in an intron of NHLH1. As technological advances in sequencing have expanded our understanding of the genome, it has become clear that introns are not merely junk DNA and that variants in introns can also affect gene expression. ³² , ³³ The SNP rs11265375C>T was predicted to affect NHLH1 promoter activity in the UCSC genome browser. A luciferase assay confirmed that the variant allele had higher NHLH1 promoter activity than the wild‐type allele. Although the role of NEUROD1 in SCLC and as an upstream regulator of NHLH1 (aliases of NSCL1) is known, the direct relationship between NHLH1 and SCLC remains unknown. ⁶ , ¹⁷ , ¹⁸ , ³⁴ Further research is required to clarify this in the future.

ICIs are therapeutic agents that are revolutionizing the treatment of lung cancer, especially NSCLC. ICIs have also brought about a paradigm shift in the treatment methods for SCLC. In recent studies, the combined use of ICI with conventional platinum doublet chemotherapy was shown to extend the median OS in patients with ED‐SCLC from ~10 months to 12 to 13 months. ³ , ⁴ As our study included patients from before ICIs were introduced as a standard treatment for SCLC, it is not known how these polymorphisms affect the clinical outcomes of patients with SCLC treated with ICI‐combination therapy. Therefore, it would be interesting to study the effects of these two polymorphisms in patients who receive ICI‐combination therapy.

This study has some limitations. First, all the patients enrolled in this study were of Korean descent; therefore, caution should be exercised in generalizing the results of this study to other ethnic groups. The frequency of SNPs varies between races and may have different effects; therefore, validation in different ethnic groups is necessary. In addition, although the variant alleles affected the promoter activity of the respective genes in the lung cancer cells, the effect of the variants on gene expression could not be confirmed in actual SCLC tissues. Unlike NSCLC, SCLC tissues are difficult to obtain because they are rarely resectable. Therefore, previous studies on SEMA6A ²⁵ , ²⁶ were also performed on NSCLC tissues or lung cancer cell lines.

In summary, we investigated the effect of genetic variants of NEUROD1 target genes on clinical outcomes in patients with SCLC. SEMA6A rs3806915C>A and NHLH1 rs11265375C>T were significantly associated with better chemotherapy response and OS. Functional studies suggested that these SNPs may influence clinical outcomes in patients with SCLC by affecting promoter activity and gene expression.

METHODS

Study population

The study population has been described in our previous study. ¹⁹ Briefly, 261 patients diagnosed with SCLC between 1997 and 2017 at the Kyungpook National University Hospital (KNUH) who received at least two cycles of the EP regimen or the IP regimen chemotherapy as first line treatment were enrolled. Patients treated with concurrent chemoradiotherapy were excluded because radiotherapy may affect the evaluation of chemotherapy response. Patients who received radiation therapy after chemotherapy were included. Treatment was discontinued in case of disease progression or major toxicity, or as determined by the patient or physician. Chemotherapy response was assessed after every two cycles of treatment by computed tomography using the Response Evaluation Criteria in Solid Tumors. Patients displaying complete or partial response to first‐line chemotherapy were classified as responders, and those with stable or progressive disease were classified as non‐responders.

This study was approved by the Institutional Review Board of the KNUH. Blood samples for genotyping were provided by the National Biobank of Korea‐KNUH, which is supported by the Ministry of Health, Welfare, and Family Affairs (approval no. KNUCH 2020‐03‐040). All blood samples were obtained before the first chemotherapy session. Informed consent was obtained from all subjects or their legal guardians. All methods were performed in accordance with relevant guidelines and regulations.

Selection of SNPs and genotyping

We selected 45 NEUROD1 target genes by searching public databases and related articles. We collected 33 917 SNPs using a public database (http://www.ncbi.nlm.nih.gov/SNP). To identify potentially functional polymorphisms, we used FuncPred utility for functional SNP prediction in the SNPinfo web server (https://snpinfo.niehs.nih.gov/). After excluding SNPs with low minor allele frequencies (≤0.1 by HapMap‐JPT data), 180 potentially functional SNPs were collected. Using the TagSNP utility for linkage disequilibrium (LD)‐tagged SNP selection, 59 LD polymorphisms (r2 ≥ 0.8) were excluded, and the remaining 121 SNPs were prepared for genotyping. We designed primers of 28plex at the multiplex level and excluded 10 SNPs during the primer combination. A three‐step polymerase chain reaction (PCR) was performed for the remaining 111 SNPs. Genotyping was performed using Sequenom MassARRAY iPLEX assay (Sequenom) following the manufacturer's instructions. Of the 111 SNPs, 100 SNPs (excluding 11 with call rates <95% or p value for Hardy–Weinberg equilibrium [HWE] <0.05) in the NEUROD1 target genes were processed for statistical analysis (Table S1).

Promoter‐luciferase constructs and luciferase assay

To verify the functional relevance of the two genetic variants, we investigated whether rs3806915C>A and rs11265375C>T regulate the promoter activity of SEMA6A and NHLH1, respectively.

A 1754 bp fragment (from −1403 to +351 bp based on the transcription start site) that included rs3806915C>A was synthesized by PCR using genomic DNA from a donor carrying a heterozygote. The SEMA6A genomic sequence was used as the PCR template, and the pGL3‐basic genomic sequence was used as the PCR template primers. The sequences of the primers used were as follows: Insert_fwd: 5′‐ttctctatcgataCGAGGCTGGCTCTTGAAGCC‐3′, Insert_rev: 5′‐agagctcggtaccTCTGCGCCGATTAACAAGTCATTTC‐3′, pGL3‐basic_fwd: 5′‐aatcggcgcagaGGTACCGAGCTCTTACGCGTG‐3′ and pGL3‐basic_rev: 5′‐gagccagcctcgTATCGATAGAGAAATGTTCTGGCACC‐3′ (the overlapping sequences of vectors and inserts are indicated by lowercase letters). PCR products were assembled into the pGL3‐basic‐SEMA6A construct containing the rs3806915 C or A allele using the NEBuilder™ HiFi DNA Assembly Master Mix Kit (New England Biolabs), according to the manufacturer's instructions. A 398 bp fragment (from +173 to +571 bp based on the transcription start site) that included rs11265375C>T was synthesized by PCR using genomic DNA from a donor carrying a heterozygote. The forward primer with the Kpn I restriction site (5′‐CGGGGTACCCTAGAAAGCTGGTCACTAAC‐3′) and reverse primer with the Xho I restriction site (5′‐CCGCTCGAGGCAGCAGCTTCTATTTACCC‐3′) were used. The PCR products were cloned into the Kpn I/Xho I site of the pGL3‐basic vector (Promega), resulting in pGL3‐basic‐NHLH1 constructs containing either rs11265375 C or T alleles. All constructs were verified by genome sequencing before use.

H446 lung cancer cells were transfected with the pRL‐SV40 vector (Promega) and the pGL3‐basic vector using Lipofectamine™ (Qiagen). The cells were harvested 48 hours following transfection, and lysates were prepared using the Dual‐Luciferase Reporter Assay System (Promega). Luciferase activity was measured using a Synergy HTX Multi‐Mode Microplate Reader (BioTek Instruments), and the activity was normalized to that of pRL‐SV40 Renilla luciferase activity.

Statistical analysis

The Statistical Analysis System version 9.2 for Windows (SAS Institute) software was used for statistical analysis. Response to chemotherapy was analyzed as the proportion of responders and non‐responders based on clinical variables and genotypes. OS was defined as the period from the day of the first chemotherapy to the date of patient death or last follow‐up. The estimated OS based on the clinical variables and genotypes was analyzed using the log‐rank test and Kaplan–Meier method. Adjusted hazard ratios (aHR) and 95% CIs were calculated for the multivariate statistical models (Cox proportional hazards models). Adjustment variables were as follows: age, sex, smoking status, clinical stage, Eastern Cooperative Oncology Group performance status, weight loss, chemotherapy regimen, second line chemotherapy, neuron‐specific enolase, and radiation to the tumor.

AUTHOR CONTRIBUTIONS

Conceived and designed the experiments: Hyo‐Gyoung Kang and Jae Yong Park. Performed the experiments: Sunwoong Lee, Hyo‐Gyoung Kang, Jin Eun Choi, Mi Jeung Hong, Sook Kyung Do, and Jang Hyuck Lee. Acquired clinical data: Seung Soo Yoo, Won Ki Lee, Ji Eun Park, Sun Ha Choi, Hyewon Seo, Jaehee Lee, ShinYup Lee, Seung Ick Cha, Chang Ho Kim, and Jae Yong Park. Analyzed and interpreted the data: Sunwoong Lee, Seung Soo Yoo, Hyo‐Gyoung Kang, and Jae Yong Park. Wrote the main manuscript text: Sunwoong Lee, Seung Soo Yoo, Hyo‐Gyoung Kang, and Jae Yong Park. Supervised the study: Hyo‐Gyoung Kang, and Jae Yong Park. All authors reviewed the manuscript.

FUNDING INFORMATION

Kyungpook National University Development Project Research Fund 2020.

CONFLICT OF INTEREST STATEMENT

The authors declare no competing interests.

Supporting information

Table S1. Summary of selected 100 SNPs in NEUROD1 target genes and response to chemotherapy and overall survival.

Lee S, Yoo SS, Choi JE, Hong MJ, Do SK, Lee JH, et al. Genetic variants of NEUROD1 target genes are associated with clinical outcomes of small‐cell lung cancer patients. Thorac Cancer. 2023;14(13):1145–1152. 10.1111/1759-7714.14839

DATA AVAILABILITY STATEMENT

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