Integrative genomic profiling reveals characteristics of lymph node metastasis in small cell lung cancer

Introduction

Small cell lung cancer (SCLC) is a heterogeneous neuroendocrine tumor originating from the Kulchitsky cells of the bronchial mucosa epithelium. As the most aggressive subtype, SCLC accounts for about 15–20% of all lung cancers, and more than 70% of patients are already in the extensive stage of SCLC (ES-SCLC) at the time of admission (1). However, the first-line standard treatment of etoposide combined with cisplatin or carboplatin has a poor prognosis (2,3); the median survival time is only 10 months, and the 2-year survival rate is less than 5% (4). SCLC is a recalcitrant and highly metastatic cancer (5). Patients with lung-confined local SCLC may benefit from surgical resection to remove their primary tumor, which has been linked to a higher survival rate (6). Nearly 70% of SCLC patients, on the other hand, already have metastatic disease when they are diagnosed, most commonly in the lymph nodes (LNs), brain, liver, and bones (5,6).

LNs are the central transport hub of circulating immune cells, which exist widely in the whole body (7). Tumor cells can metastasize to LNs and quickly spread to other organs. Regional LNs are the most common metastatic site for SCLC, and lymph node metastasis (LNM) usually occurs in the early stage of SCLC. Metastasis of cancer cells from the primary tumor to regional LNs is a major factor in poor prognosis (5). Although chemotherapy and irradiation can help to reduce the development of SCLC metastases, they do not continuously improve disease-free survival and overall survival (OS) (8). This is particularly important for early detection and the provision of reliable therapeutic targets. A large number of studies have found that some novel differentially expressed genes (DEGs) are closely related to LNM in lung cancer, including SCLC (9-11). Recently, Chen et al. explored the characteristics of gene mutations in non-small cell lung cancer (NSCLC) with and without LNM (12). However, research into the genomic landscape or multi-omics of SCLC with various LNM statuses is limited. A high mutation rate and genomic instability almost certainly contribute to SCLC’s characteristic behavior (13). The high mutational burden caused by cigarette smoking, as well as the rapid tumor growth observed in SCLC patients with metastatic disease, indicates that primary SCLC tumors are inherently metastatic (14). The identification of markers for the various mechanisms by which premetastatic SCLC cells become metastatic will help personalize treatment for metastatic SCLC subtypes. Analysis of genomic or transcriptomic alterations is critical for understanding the molecular processes of LNM in lung cancer (15,16).

In this study, we evaluated tissue specimens by whole-exome sequencing (WES) and RNA-sequencing to investigate the association between genomic and transcriptomic alterations and the LNM in 26 patients with SCLC. We characterized and compared the key molecular features between patients with (N+) and without (N0) LNM from patients who had been operated for SCLC. Multi-omics analyses were integrated to identify significantly mutated genes (SMGs) and DEGs associated with prognosis. To our knowledge, this is the first integrative genomics analysis of the molecular characteristics of LNM in SCLC. Our findings could have a variety of implications for tumorigenesis, immunotherapy, and prognosis for SCLC patients with LNM. We present the following article in accordance with the STREGA reporting checklist (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-22-785/rc).

Methods

Participants

A retrospective screen was conducted on formalin-fixed, paraffin-embedded (FFPE) and fresh frozen tumor tissues from SCLC patients who underwent surgery in the Tongji Hospital of Huazhong University of Science and Technology between May 2018 and August 2019. The patient inclusion criteria were as follows: (I) pathologically diagnosed with pathologic stage T1–T4, N0–3, M0 SCLC according to the 2015 (4^th) edition of the World Health Organization (WHO) guideline during the operation; (II) without underlying cardiovascular, urinary, hepatic, and blood system disease; (III) expected survival time greater than 3 months; and (IV) complete medical records available. The exclusion criteria were as follows: (I) SCLC was not the first primary cancer; (II) sample collection, storage, or test operation was not qualified; and (III) incomplete patient information. We defined OS as the time interval between surgery and death from any cause or the final follow-up visit (censored patient). The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). This study was approved by the Institutional Review Board of Tongji Hospital of Huazhong University of Science and Technology (No. TJ-IRB20220329) with the written informed consent of all enrolled patients.

WES and analysis

The FFPE sections were evaluated by pathologists to confirm that more than 50% of cells were tumor cells. Matching normal tissue was recovered >2 cm from the visible margin of the tumor, without having any tumor cells, according to histopathologic assessment. Using the QIAamp DNA FFPE tissue kit (Qiagen, Germantown, MD, USA), DNA was extracted from 10–15 unstained FFPE sections of 5 µm thickness and assessed using Nanodrop and Qubit (Thermo Fisher Scientific, Waltham, MA, USA). We prepared exon-wide capture libraries using the TruePrep DNA Library Prep Kit V2 (#TD501, Vazyme, Nanjing, China) and xGen^® Exome Research Panel (Integrated DNA Technologies, Inc., Redwood City, CA, USA). The paired-end sequencing data with an average coverage of 130 for controls and 200 for tumors were generated using an Illumina HiSeq system (Illumina, San Diego, CA, USA). GATK 4.0 (Broad Institute, Cambridge, MA, USA) was used to sort and remove polymerase chain reaction (PCR) duplication, and Burrows-Wheeler aligner (BWA) was used to align the sequences to the human reference genome (NCBI build 37).

We used Strelka2 (Illumina) to detect single nucleotide variants (SNVs), insertions, and deletions (indel) with default parameters. Ensembl Variant Effect Predictor (VEP; https://asia.ensembl.org/info/docs/tools/vep/index.html) was used to annotate variants and polymorphisms. The minimum reads to support somatic SNV/indel calling was 20 for a mutated region and 5 reads for a variant. In the matched normal sample, ≥20× of coverage and <5 reads of a variant at the same site were required. Variants having a minor allele frequency (MAF) >1% were filtered out from the ExAC, gnomAD, and esp6500 databases as common germline variants. MutSigCV software on the GenePattern platform (https://www.genepattern.org/) was used to identify SMGs. The driver gene list was established by combining 2 sets from previous studies (17,18). The ANNOVAR annotation of mutations was conducted with cosmic89_coding, oncoKB, and other tools. Somatic copy number variants (CNVs) identified by FACETS and recurrently occurring CNVs were detected with GISTIC2.0 (19).

RNA sequencing and analysis

Total RNA extracted from the freshly frozen tissue samples. We constructed RNA-sequencing libraries with the TruSeq RNA Library Preparation Kit (Illumina) following the manufacturer’s instructions. The resulting libraries qualified using Bioanalyzer 2100 (Agilent, Santa Clara, CA, USA) and quantified using real time-PCR were loaded on the Illumina NovaSeq 6000 for sequencing. The sequencing reads preprocessed using Trimmomatic were aligned to the annotation files of the human genome (UCSC hg19) using STAR with default parameters (20). The human genome sequence was downloaded from Gencode. Reads of each gene were counted using htseq-count, and cufflinks was used to calculate the fragments per kilobase of exon per million mapped fragments (FPKM) from the aligned bam files (21). The DEGs between normal and tumor samples were qualified using R package DESeq2.

Weighted gene co-expression network analysis (WGCNA)

Based on the WGCNA algorithm of the R project (Langfelder and Horvath, 2008), the screened DEGs were prepared for further scale-free network construction. Firstly, the appropriate soft threshold power β was determined by pickSoftThreshold function. Then Pearson’s correlation of gene expression profiles was calculated, similarity of gene expression patterns was evaluated, and adjacency matrix was established. After the co-expression network is completed, the dynamic tree cutting algorithm were used to detect the module. Finally determine the modules that meet these criteria and generate the corresponding tree diagram.

Tumor microenvironment (TME) analysis and multi-omics integration analysis

Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) is a deconvolution algorithm of R package that estimates the absolute abundance of 22 human immune cell populations using gene expression profile data (22). It was utilized to explore immune cell abundance in SCLC tissues. Multi-omics integration analysis at the gene level was performed using CNAmet (23), which is an R package that integrates copy number alteration (CNA), gene expression, and DNA methylation information. In this study, CNAmet was used to calculate a wise weight score for a gene, indicating gene alterations due to changes in copy number levels.

Statistical analysis

The software SPSS 25.0 (IBM Corp., Armonk, NY, USA) was used for all statistical analysis. Normally distributed continuous data were tested using analysis of variance (ANOVA), and non-normally distributed continuous data were analyzed using the Wilcoxon test. Student’s t-test was used for continuous variables with normal distributions. Fisher’s exact test and the chi-square test were used for categorical variables. Kaplan-Meier survival and Cox regression were used to evaluate the OS, and the log-rank test was used to analyze the differences. Statistical significance was considered when P<0.05.

Results

Clinical characteristics

In this study, 26 qualified SCLC patients (15 N+ and 11 N0) were included in the retrospective analysis. Pathology examination showed that among the 26 resected SCLC tissues, 3 (11.5%) were diagnosed with combined SCLC (a rare subtype of SCLC; a heterogeneous tumor composed of SCLC and non-small cell carcinoma), and the remaining 23 (88.5%) patients were diagnosed with pure SCLC. Patient clinical characteristics are summarized in Table 1. The mean age of the participants at diagnosis was 59 years, and 23 (88.5%) patients were male. Some 12 patients were positive and 13 patients were negative for programmed death ligand-1 (PD-L1) staining. Interestingly, we found that LNM in our cohort may be associated with smoking (P=0.040).

Mutational landscape of N0 and N+ patients

The results of WES revealed a total of 9,904 nonsynonymous somatic mutations in 6,294 genes, with the most common variants occurring in TTN (85.0%), TP53 (81.0%), and MUC16 (58.0%) (Figure S1). Of these genes, the N0 tumors had 3,386 mutated genes, while the N+ tumors had 4,101 mutated genes, with only 18 common mutations between the groups, indicating distinct mutation profiles related to LNM. As shown in Figure 1A, we summarized the top 30 driver genes with the highest frequency of somatic mutations. In descending order of mutation frequency, the top 5 genes were TP53, APOB, FAM135B, SPTA, and ZNF429 in the N0 group, and TP53, LRP1B, RB1, CDH10, and KMT2D in the N+ group. Specifically, the mutation frequencies of AFF3, COL5A1, GRIN2A, UBR5, ZNF521, CDH10, and FN1 were noticeably lower in the N0 cases than in the N+ cases (Wilcoxon P<0.001), whereas FAM135B, ZNF429, POLE, ZNRF3, ERBB4, and NOTCH1 mutation frequencies were remarkably higher in the N0 cases (Wilcoxon P=0.004) (Figure 1B). For DNA damage repair (DDR) genes, the top 5 genes with the highest mutation frequency were TP53, POLE, POLQ, HFM1, and LIG4 in the N0 group, and TP53, POLE, POLQ, FEN, and PRKDC in the N+ group (Figure 1C). Interestingly, mutations in FEN1, PRKDC, and TDG occurred only in the N+ group, yet mutations in some genes, such as ANKRD28, FANCD2, and FANC1, occurred only in the N0 group (Figure 1D). Thus, these results suggest that the mutation rate of these genes is closely related to LNM in patients with SCLC. We also found that the mean value of tumor mutational burden (TMB) in the N0 and N+ groups was 7.871 mut/Mb and 7.474 mut/Mb, respectively, but the differences were not significant (P=0.24) (Figure 2A). The average weighted Genome Instability Index (wGII) score was 0.490 in the N0 group and 0.485 in the N+ group, with no significant difference (P=1) (Figure 2B).

Figure 1 Comparison of mutation landscape between N0 and N+. (A) A comparison of the mutational landscapes of N0 and N+ is provided, along with the most frequently mutated driver genes. The top panel represents the TMB and the middle panel represents the matrix of frequently mutated genes. Columns represent samples, and clinicopathological characteristics of individual patients are presented below. (B) List of driver genes with significant differences between the two groups. (C) The most frequently mutated DDR genes in the N0 and N+ cohorts were compared. (D) List of DDR genes with significant differences between the two groups. TMB, tumor mutational burden; LN, lymph node; DDR, DNA damage repair.

Figure 2 TMB, wGII, and variant spectrum in SCLC with LNM. (A) The TMB difference between N0 and N+. (B) Comparison of wGII between N0 and N+. (C) Relative contribution of six substitution subtypes SNV in each group. Colored bars represent different substitution subtypes. (D) Relative contributions of mutational signatures in each group. TMB, tumor mutational burden; wGII, weighted Genome Instability Index; SCLC, small cell lung cancer; LNM, lymph node metastasis; SNV, single nucleotide variation.

To ascertain the association between mutation frequency distribution and cosmic signature in the N0 and N+ patients, we performed non-negative matrix decomposition on the frequency of 96 substitution types. The distribution results indicated a strong preference for C>A substitutions (Figure 2C). The N+ tumors had more frequent T>A mutations than the N0 tumors (1117/8.8% vs. 659/6.8%), with no significant difference. Additionally, signatures 4, 6, and 20 were identified in both the N0 and N+ groups (Figure 2D), with signature 4 being linked to tobacco-induced mutations and signature 6/20 being linked to defective DNA mismatch repair. We also found that mutation signature 7 (associated with ultraviolet light exposure) was present only in the N+ tumors, and signature 2 (associated with activity of the Activation-induced cytidine deaminase/apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like (AID/APOBEC) family of cytidine deaminases) was present only in the N0 tumors, which suggests that mutation signatures 2 and 7 are associated with LNM.

CNV profiles of N0 and N+ patients

To discover novel CNVs involved in LNM, the significantly amplified or deleted regions of the genome were identified using GISTIC2.0. We found CNVs throughout the genome, with copy number gains being more prevalent than copy number losses. Gain of chromosomes 22q11.1, 9q13, 16p11.2, 14q11.2, and 17q21.2 and loss of chromosomes 16q12.2, 6p22.1, 16p13.3, 7p21.3, and 9q21.33 were more common (Figure 3A,3B). Some chromosomes with CNVs were only identified in the N0 group, such as 10q22.2, 1p36.13, 12q12, 16q12.2, 6p21.32, and 15q21.2, and some were only identified in the N+ group, such as 2q12.2, 16q22.2, 6p21.33, 10q24.32, 17p13.2, and 8p21.3 (Table S1). Specifically, the copy number gains were found at chromosomes 2q11.2, 17q12, and 8q12.3 in the N0 tumors, but the copy number losses occurred at these chromosomes in the N+ tumors. These chromosomal loci include many genes associated with tumor metastasis. For example, the ASPH gene at 8q12.3 has been reported as associated with metastasis in several tumors (24), indicating that the CNVs at these chromosomal regions might play pivotal roles in LNM in SCLC. Additionally, the N0 tumors exhibited more CNV burden (36.35/case) than the N+ tumors (33.24/case), although the difference was not significant (P=0.44) (Figure 3C).

Figure 3 Distinct CNA landscape of N0 and N+. (A,B) Somatic CNAs in each group. Deletions and amplifications are represented on the y-axis by blue or red bars, respectively. Each peak region (cytoband) is displayed together with its known or potential cancer-related genes. (C) Comparison of the CNA burden between N0 and N+. CNA, copy number alteration.

Transcriptomic landscape of N0 and N+ patients

To better understand LNM in SCLC, we conducted a comparative transcriptomic analysis. To analyze the DEG levels between the N+ and N0 groups, the relative gene expression levels were subdivided into upregulated and downregulated. Hierarchical clustering analysis on all DEGs of each tumor sample revealed that the N+ and N0 groups shared 6,666 DEGs. Of these, 3,790 were upregulated and 2,876 were downregulated. A total of 3,710 DEGs were only observed in the N+ groups and 1,741 private DEGs only in the N0 groups (Figure 4A). These results show that the expression levels of melanoma-associated antigen A4 (MAGEA4), forkhead box I3 (FOXI3), potassium voltage-gated channel subfamily Q member 2 (KCNQ2), and fibroblast growth factor 21 (FGF21) were only significantly increased in the N+ patients compared with the N0 patients. Relaxin family peptide receptor 2 (RXFP2), bactericidal/permeability-increasing-fold-containing family B member 1 (BPIFB1), thyrotropin releasing hormone degrading enzyme (TRHDE), and ankyrin repeat domain 28 (ANKRD28) were reduced in the N+ patients. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment analyses of DEGs showed differences in signaling pathways between the N+ and N0 groups (Figure 4B,4C). The shared DEGs were enriched in well-known carcinogenesis pathways including “focal adhesion”, “MicroRNAs in cancer”, and “extracellular matrix (ECM)-receptor interaction”.

Figure 4 The transcriptomic landscape of N0 and N+ patients. (A) Heatmap of DEGs. Hierarchical clustering analysis of DEGs from each N+ and N0 tumor sample. Blue indicates the lowest amount of gene expression, whereas pink-orange indicates the highest level. (B) KEGG pathway enrichment of the DEGs. The y-axis represents the pathway name, while the x-axis represents the enriched factor in each pathway. (C) GO enrichment analysis of DEGs. Top 10 GO terms in CC, BP and MF are presented. The size of the bubbles reflects the number of genes, while the color bar indicates the corrected P value, with a greater value in red and a lower value in blue. LN, lymph node; ECM, extracellular matrix; ABC, ATP-binding cassette; ATP, adenosine triphosphate; CC, cellular component; BP, biological process; MF, molecular function; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; DEG, differentially expressed gene.

In addition, the top 5,000 genes with an absolute median difference were obtained, and a co-expression network was constructed using weighted correlation network analysis (WGCNA). The appropriate soft threshold power β was determined as 12 for subsequent adjacencies calculation (Figure S2A). Finally, five gene modules were obtained according to the results of the dynamic tree cut algorithm (Figure S2B). The relationship of modules and clinical features were evaluated and visualized in Figure S2C to explore the clinical significance of co-expressed genes. The yellow module in the figure was significantly associated with smoking and gender. Furthermore, the correlation of module membership and gene significance for LNM in grey module is depicted in Figure S2D.

TME of N0 and N+ patients

To further study the relationship between immune cell characteristics of SCLC and LNM, TME expression profiles were clustered as N+ and N0 groups. The clusters presented different immune cell profiles including signatures of T cells, B cells, macrophages, and natural killer cells (Figure 5A). Briefly, the mean proportions of plasma cells and CD8⁺ T cells in both the N+ and N0 groups were over 10%, whereas CD4 naïve T cells and T cells gamma delta were absent in all samples. There were significantly more T regulatory cells (Tregs) (P=0.045) (Figure 5B) and M0 macrophages cell fractions (P=0.074) in the N+ group (Figure 5C), yet the activated dendritic cell fraction was greater in the N0 group (P=0.09) (Figure 5D).

Figure 5 TME differences in N0 and N+. (A) Distinct immune cell signatures between N0 and N+. (B-D) Significantly different proportions analysis of immune cell types from each group. Fractions of Tregs (B) and M0 macrophages (C) were significantly more profound in N0 than in N+. Fraction of activated dendritic cells (D) was significantly more profound in N+ than in N0. The KEGG enriched with the germline-regulated genes. TME, tumor microenvironment; Tregs, T regulatory cells; KEGG, Kyoto Encyclopedia of Genes and Genomes.

Analysis of multi-omics integration

To better understand the characteristics of LNM in SCLC, we integrated our analysis of the mutation types, copy number alterations, and mRNA expression. We found that CNV gain of AFF3 and TDG was prevalent in SCLC, and that CNV loss occurred more frequently in ANKRD28 and RB1. Interestingly, ANKRD28 expression was consistently lower in the N+ tumors than in the N0 tumors (Figure 6A). As shown in Figure 6B, mRNA levels of RB1 (P=0.0087), AFF3 (P=0.058), TDG (P=0.05), and ANKRD28 (P=0.042) were significantly associated with the copy number alterations. Of these genes, AFF3, TDG, and ANKRD28 mutations were closely related to LNM in our cohort. Our findings suggest that variables other than mutation status and CNV may alter gene expression.

Figure 6 Correlation of mRNA expression with CNV status. (A) Distribution of RB1, ANKRD28, AFF3, and TDG expression and CNV across samples. (B) Correlation between mRNA expression and CNV of RB1, ANKRD28, AFF3, and TDG. mRNA, messenger RNA; CNV, copy number variation.

Prognostic analysis of N0 and N+ patients

During the median 23-month post-surgery follow-up, 8/26 (30.8%) patients died. We found no significant correlation between OS and LNM in our cohort (P=0.82) (Figure S3A). Additionally, neither CNV, wGII, nor TMB had a significant impact on OS in N+ and N0 patients (Figure S3B-S3D). We further found that presence of the ZNF521 mutation was substantially related to prognosis (OS, P<0.0001) (Figure S4A), which was significantly associated with LNM in our cohort. In N+ patients, rare TENM4 (P=0.011), DNAH9 (P<0.0001), AHDC1 (P=0.038), DNER (P=0.045), and ADAMTS18 (P=0.037) mutations were associated with worse prognosis (Figure S4B-S4F). In the N0 patients, OS was significantly shortened in patients with NPHS1 (P=0.024), PPFIA3 (P=0.019), MUC3A (P=0.019), AGBL1 (P=0.015), ATP8B3 (P=0.024), and MYH14 (P=0.021) mutations (Figure S5). Furthermore, differential expression of MAGEA4 and TRHDE between the N+ and N0 patients was substantially related to OS (log-rank test; P=0.00029 and P=0.011) (Figure 7).

Figure 7 Association of MAGEA4 and TRHDE expression status and clinical outcomes. (A) Survival analysis of MAGEA4 expression and survival outcomes (P=0.00029); (B) survival analysis of TRHDE expression and survival outcomes (P=0.011).

cBioPortal data analysis

Genomic characteristics were further validated using cBioPortal datasets (N0 group, n=47; N+ group, n=59). The 3 most frequent somatic alterations were identified in TP53 (86%), RB1 (71%), and TTN (63%) (Figure 8A). Specifically, the mutation frequencies of DST (P=0.019), SALL3 (P=0.034), TAF1L (P=0.034), GPRC6A (P=0.034), GALNT17 (P=0.035), and TNR (P=0.038) were noticeably higher in the N+ cases than in the N0 cases. Similarly, no significant difference in TMB was found between the N+ and N0 groups (Figure 8B). We found that POLE (P=0.005169) and ZNF429 (P=0.001075) mutations were associated with worse prognosis (Figure 8C). Further, LNM was significantly associated with poor prognosis (P=0.014) (Figure 8D).

Figure 8 Genomic characteristics analysis of SCLC with LNM in the cBioPortal data set. (A) Landscape of somatic mutations in each group. (B) Compared TMB difference between N0 and N+. (C) Association of SMG alterations and worse prognosis in the cBioPortal cohort. The left shows POLE mutations (P=0.005169). The right shows ZNF429 mutations (P=0.001075). (D) Survival analysis of LNM status and clinical outcomes (P=0.014). TMB, tumor mutational burden; SCLC, small cell lung cancer; LNM, lymph node metastasis; SMG, significantly mutated genes.

Discussion

Studies on LNM in SCLC have mainly focused on the transcriptome level (11,16); thus a multi-omics analysis is lacking in the literature. In this study, we performed genomic and transcriptomic profiling of SCLC patients. By integrating multi-omics data and clinical features, we provide a comprehensive view of the characteristics of LNM in SCLC.

Except for TP53 and RB1, there is little consensus regarding shared gene mutations among SCLC patients. In our study, the most common mutations were found in TTN and TP53 genes, and we found a high frequency (69.2%) of a TTN/TP53 double mutation. The results were further corroborated using cBioPortal data, which showed a high frequency (55.7%) of TTN/TP53 co-mutation. Mutation of the TTN gene has been significantly associated with lung squamous cell carcinoma (LUSC) (25); LUSC patients have favorable OS benefits from TTN mutation or TTN/TP53 double mutation. However, the identification of TTN as an important cancer-related gene, especially in SCLC, remains controversial since mutations have only been demonstrated through bioinformatics analysis. The focus of this controversy lies in its large complex structure and the false positive results caused by heterogeneity in the mutation process (26,27). By combining our data with previous SCLC studies, we were able to identify other prevalent mutant driver genes with a prevalence of more than 20%, including LRP1B, KMT2D, and CDH10. Furthermore, the significantly mutated driver genes were identified in LNM and non-metastasis groups, some of which were specifically associated with LNM, including ZNF521, CDH10, ZNF429, and FAM135B. Recurrent CDH10 mutations have been reported in non-smokers who are negative for EGFR/KRAS/ALK mutations in lung adenocarcinoma (28) and as a prognostic signature in colorectal cancer (29). Jiang et al. showed that, as the most common and significant mutant gene, CDH10 is also related to poor survival in SCLC (30). Both ZNF521 and FAM135B have also been reported to be associated with LNM in other cancers (31,32). Further studies on the relationship between these differentially mutated genes and prognosis showed that ZNF521 was significantly associated with prognosis of SCLC patients with LNM. In addition, we found that rare mutations in TENM4, DNAH9, AHDC1, DNER, and ADAMTS18 were associated with poorer prognosis in patients with lymph node metastasis, while patients without metastasis with mutations in NPHS1, PPFIA3, MUC3A, AGBL1, ATP8B3, and MYH14 had significantly shorter OS. Although this conclusion should be confirmed with additional research and larger sample size.

Cosmic signature analysis revealed widespread defective DNA mismatch repair and DDR gene alterations in our cohort. The initial high response rate of SCLC to platinum (cisplatin and carboplatin) and topoisomerase I/II inhibitors may be explained by the high-proliferative capacity of the tumor cells and the evidence indicating that DDR pathway defects are more susceptible to DNA damage-inducing treatments, such as chemotherapy and radiotherapy (etoposide and irinotecan). The Fanconi anemia (FA) pathway is critical for repairing DNA damage caused by inter-strand crosslinkers such as cisplatin. It has also been shown that mutations in FA pathway genes may lead to initial hypersensitivity to platinum-based therapy (cisplatin) in SCLC (30,33). In our cohort, most patients received conventional chemoradiotherapy after surgery, but there was no significant association between chemoradiotherapy and prognosis. Since various chemotherapeutics require a functional TP53 protein to effectively induce apoptosis, the functional loss caused by high frequency mutations of TP53 may enhance resistance to cytotoxic drugs in SCLC (34,35). In addition, the combination of TP53 inactivation and MMR defects or other DNA repair mutations has also been observed to lead to early sensitivity and potential resistance of SCLC to cisplatin (30,36). This resistance requires urgent attention to improve clinical diagnosis and treatment of SCLC.

Over 95% of patients with SCLC have a history of smoking, with a 5-year survival rate of less than 2% (37). In our cohort, a history of smoking in 74% of patients was significantly associated with LNM, further verified by cosmic profiling. Tobacco is known to contain various addictive compounds, including nicotine, which binds to the nicotinic acetylcholine receptor (nAChR). Studies have shown that nicotine induces the expression of achaete-scute family bHLH transcription factor 1 (ASCL1), which in turn induces the expression of the nAChR gene cluster (38,39). In particular, α3, α5, and β4 nAChR subunits (e.g., CHRNA5/A3/B4) encoded by the nAChR gene cluster are differentially expressed in SCLC (40). There is even an emerging consensus to classify SCLC subtypes based on the differential expression of four transcription factors: ASCL1 (SCLC-A), NEUROD1 (SCLC-N), POU2F3 (SCLC-P), and YAP1 (SCLC-Y) (41). Similarly, ASCL1 and CHRNA5/A3/B4 were found to be over-expressed in our cohort with a greater than 4-fold change in expression, and their differential expression changes were greater in LNM patients than in those without metastasis. Chan et al. found that SCLC-A subtype showed less T-cell dysfunction than SCLC-N subtype using single-cell transcriptome sequencing. SCLC subtypes have the potential of plasticity and mutual transformation, especially SCLC-A and -N subtypes. In addition, SCLC showed greater immune isolation and less immune invasion than lung adenocarcinoma (42). Transcriptome data were also used to analyze and compare DEGs in the N0 patients and N+ patients. Several DEGs that have been reported to be associated with tumor metastasis were differentially expressed between the N+ and N0 groups, including MAGEA4, FOXI3, and TRHDE. As a cancer/testis antigen (CTA), MAGEA4 is overexpressed in a variety of cancers. Previous studies have found that MAGEA4 overexpression is associated with LNM of various tumors (43-45). Yoshida et al. found that MAGEA4 expression was negatively correlated with survival in NSCLC patients (46). In our study, the presence of differentially expressed MAGEA4 and TRHDE were also significantly associated with OS. Previous reviews indicated that the expression levels of FOXI3 were higher in bone and lung metastases and LNM (47,48). The FOXI3 target gene is also associated with breast cancer metastasis (49). In addition, Mukherjee et al. analyzed the expression data of the time from occurrence of primary breast tumors to distant metastasis and found that tumors with the highest expression of FOXI3 characteristic genes metastasized earlier than those with the lowest expression (47). Similarly, FOXI3 as a DEG was higher in the N+ patients but not differentially expressed in the N0 patients, supporting previous reports. Previous study has indicated that in patients with stage IA lung adenocarcinoma, especially when CEA level is over 5 ng/dL and SUVmax is over 5, the risk of lymph node metastasis is higher, indicating the predictive factors of lymph node metastasis in clinical stage IA lung adenocarcinoma (50). Here we identify factors associated with SCLC lymph node metastasis at the molecular level by integrating genomic and transcriptomic data, which is the first report to our knowledge.

Our study had several limitations that should be noted. First, our sample size was small. Thus, it is necessary to verify the results of this study in the future with more patients and a longer follow-up period, as well as functional studies of key genes. Second, because this was a retrospective study, there was a possibility of bias in the case selection and limited medical information about the patients. Third, our study lacked proteomics and epigenetics analysis, which may provide more molecular information related to SCLC with LNM. In addition, the mechanism of early sensitivity and potential resistance to radiotherapy or chemotherapy for SCLC is still unclear, which needs to be further explored with a larger sample size in the future. Despite these limitations, this is the first integrative genomics profiling of LNM in SCLC and our results provide a comprehensive view of the genomics characteristics of LNM in SCLC. Our findings are particularly important for early detection and the provision of reliable therapeutic targets, although these targets still require extensive studies to be validated in the future.

Conclusions

We used WES and RNA sequencing to identify SMGs and DEGs related to LNM in SCLC and to further evaluate the link between these variants and clinical characteristics and prognosis. Our findings are particularly important for early detection of metastases in SCLC and the provision of reliable therapeutic targets.

Acknowledgments

We thank Shanghai Tongshu Biotechnology Co., Ltd. for technical support. The authors also appreciate the academic support from the AME Lung Cancer Collaborative Group.

Funding: None.

Footnote

Reporting Checklist: The authors have completed the STREGA reporting checklist. Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-22-785/rc

Data Sharing Statement: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-22-785/dss

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-22-785/coif). All authors report technical support from Shanghai Tongshu Biotechnology Co., Ltd. SKJ receives consulting fees from Merck & Co, Inc., Novocure, Radialogica, and grants from Beigene and Merck & Co, Inc., and is a reviewer for IMX Medical, on the adjudication committee for Syntactx and participates in a DMSB for Advarra. The authors have no other conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The studies was reviewed and approved by the Institutional Review Board of Tongji Hospital of Huazhong University of Science and Technology (No. TJ-IRB20220329). The patients provided their written informed consent to participate in this study.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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(English Language Editor: J. Jones)