Distinct mutation landscape with similar immune microenvironment in primary simultaneous operable squamous cell carcinoma and peripheral-type small-cell lung cancer

Introduction

Lung cancer leads to most cancer-related deaths worldwide according to cancer statistics (1). The number of patients with multiple primary lung cancers (MPLCs) is increasing in the clinic with the development of cancer screening technology (2), and most of these studies are focused on synchronous and/or metachronous multiple primary lung adenocarcinomas. Molecular biology studies of MPLCs of the same pathological type in one patient revealed unique mutation profiles (3). To date, studies of patients with MPLC with both primary small-cell lung cancer (SCLC) and squamous cell carcinoma (SQ) have rarely been reported, and the biology remains unclear. The literature thus far has been based on limited reports (4,5), which is an urgent clinical need because combined-histology lung cancers with both primary simultaneous SQ and SCLC make treatment decisions difficult in practice.

SCLC is closely related to a smoking lifestyle; only a very small proportion of patients with SCLC are nonsmokers (6). SQ is a common histological subtype of non-small-cell lung cancer (NSCLC), and its development is also associated with tobacco exposure, which is similar to SCLC. Detailed analyses of the biology of their genetic aberrations and microenvironment should lead to a better understanding that can be more successfully exploited for therapeutic intervention. Recent multiomics profiling, including comprehensive genome, epigenome, and proteome analyses of the length and breadth of the molecular characteristics of SQ and SCLC, respectively, has led to significant progress in revealing aspects of the biology of this type of MPLC with synchronous primary SQ and SCLC, defining new therapeutic strategies and offering renewed hope for patients. Multiple next-generation sequencing (NGS)-based studies have revealed that the loss of the tumor suppressors TP53 and RB1, including mutations and downregulation, is essential for SCLC development (7-10); correspondingly, the genes encoding NFE2L2, PTEN, MLL2, FAT1, NOTCH1 and KDM6A are mutated in SQ (11).

With the application of immunotherapy, studies on the immune microenvironment and its association with genomic and epigenetic changes in SQ and SCLC provide promising insights, and accumulating studies carried out in SQ and SCLC have revealed crucial roles of the epigenetic machinery in modulating immune cell functions and the antitumor immune response (12-14). The combination of tumor mutation burden (TMB) and PD-L1 expression or CD8+ tumor-infiltrating lymphocyte (TIL) density could stratify patients with SQ into two groups with distinct prognoses (15). Moreover, the density and diversity of tumor-infiltrating immune cells are strongly associated with survival outcomes and predict therapeutic efficacy in patients with SCLC (16). The long-term survival of patients with SCLC over four years has been found to be positively correlated with the presence of TILs in the tumor microenvironment (TME) and increased antitumor immunity (17). Further mechanistic research has indicated that the roles of tumor immunogenicity and immune cell infiltration in the antitumor response may be influenced by changes in the epigenome (14).

Unlike separate SQ and SCLC, in-depth multiomics profiling, including genomic and epigenetic characterization and TME analysis, is urgently needed to better understand this type of MPLC with both primary simultaneous SQ and SCLC. In the present study, we investigated the clinical features and mutation profiles of three patients with primary simultaneous resectable SQ and SCLC, which was confirmed by imaging and pathological characteristics. Using multiomics technology, we observed obvious mutual exclusion of each independent tumor using whole-exome sequencing (WES) and prognosis-related immune infiltration in lymphadenectases by epigenome and multiplex immunofluorescence (mIF) analyses in these patients. Our study may guide and benefit treatment strategies for this type of lung cancer patient.

Methods

Patients and samples

A total of three patients from Shanghai Chest Hospital between September 2017 and March 2019 with simultaneous ipsilateral early-stage primary SQ and peripheral-type SCLC were included in this study. The clinical information of these three patients is summarized in Table 1. Tumor samples, including primary lung cancer, metastatic lymph node (MLN) and normal lung tissue samples, were collected and stored at −80 ℃ immediately after surgery. A total of three paired tissue samples, including primary SQ, primary SCLC, metastatic lymphadenectases, and paired normal tissues, from each patient were used for WES, DNA methylation profiling, and TME analysis using mIF. Patients with pathologically confirmed primary SCLC, primary SQ and MLNs were identified by two independent pathologists, and relevant clinical data were collected from their medical records. Overall survival (OS) was defined as the time between the date of surgery and the date of death from any cause or the last follow-up of October 2024.

The current single-center study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Institutional Review Board (IRB) of Shanghai Chest Hospital (ethics approval No. KS21019). All the patients signed IRB-approved written informed consent forms and were further enrolled in this study, allowing for the collection and genomic analysis of tissue samples.

DNA extraction and WES

DNA was extracted from the tumor and normal tissues using a QIAamp DNA FFPE Tissue Kit and a QIAamp DNA Mini Kit (Qiagen, Hilden, Germany). Agarose gel electrophoresis and an Agilent 2100 bioanalyzer (Agilent Technologies, Inc., Santa Clara, CA, USA) were then used to validate the quantity and quality of the extracted genomic DNA. After DNA extraction, an S220 focused ultrasonicator (Covaris, Inc., Woburn, MA, USA) was used to shear the DNA into fragments, which were then used for NGS library construction. The NGS library was constructed according to the recommendations from Illumina and included the following procedures: end repair, dA tailing, adaptor ligation, PCR enrichment and purification. The resulting library was mixed and reacted with the exome capture probes (SeqCap EZ Exome Probes v3.0) produced by Roche. The captured library was then amplified and purified, after which it was ready for sequencing. NGS was performed using an Illumina HiSeq, and 2×50 bp sequencing reads were generated. Average sequencing depths of 135.6× for normal tissues, 122.5× for SQ samples, 118.0× for SCLC samples and 163.5× for MLNs were ultimately generated.

Sequencing data preprocessing

The adapters from the raw fastq files were trimmed using Trimmomatic (18) and mapped to the human reference genome (version hg19) using the Burrows-Wheeler Alignment tool (BWA) (19). The resulting bam files were then sorted and indexed. Duplication marking was performed with Picard software. The Genome Analysis Toolkit (GATK) (20) developed by the Broad Institute was used for local realignment around indels and base quality score recalibration. The output bam file from GATK was then forwarded for subsequent somatic mutation analysis.

Somatic mutation analysis and mutation annotation

The somatic module of VARSCAN2 software was used to call somatic mutations from normal tissues and the corresponding tumor tissues, including the primary SQ, primary SCLC and MLNs. The parameters were as follows: -min-coverage 10, -min-var-freq 0.08, and -somatic-P-value 0.05. The processSomatic module of VARSCAN2 was then used to call high-quality somatic mutations. The fpfilter and bam-readcount modules of VARSCAN2 were used to remove false positives. We further applied the following criteria to filter single-nucleotide variations (SNVs): (I) depth in tumor tissue >10, (II) depth in normal tissue >8, (III) alternative alleles in the normal fraction <0.05, and (IV) alternative allele proportion in the normal sample <0.30 of that in the tumor sample. We applied the following criteria to filter out indels: (I) depth in tumor tissue >10, (II) depth in normal tissue >8, (III) alternative allele in normal tissue =0, and (IV) alternative allele in tumor tissue >5. The resulting vcf files containing the somatic mutations were then annotated with VEP (the Ensembl Variant Effect Predictor) (21) and transferred to the MAF format.

Data summary and visualization

The Venn diagrams used to show the overlapping and specific mutation sites between tumor samples were generated using TBtools (22). The CoMut figures used to show the mutation frequencies of genes and the mutation details of the samples were summarized and generated using Maftools (23). The MAF files resulting from the previous step were imported into Maftools, and a mutation summary was generated and graphically displayed. The lolliplot, with the detailed gene mutation sites, was also generated using Maftools. Potential cancer evolution was predicted using the MesKit package (24). Gene Ontology (GO) enrichment analysis and generation of related figures were performed using ClusterProfiler (25).

DNA methylation profiling and data analysis

DNA methylation profiling was performed by Sinotech Genome Technology Co., Ltd. (Shanghai, China). DNA was bisulfite converted after extraction from the tissues, quantified and quality validated as described in “DNA extraction and WES”. An Illumina Infinium Methylation EPIC BeadChip (850K DNA methylation chip) was used to perform whole-genome DNA methylation profiling of the bisulfite-converted DNA. The data generated by the methylation chip were first quality controlled using GenomeStudio and BeadArray Controls Reporter as recommended by Illumina. Probe filtering and data normalization followed by batch effect detection and correction were performed. Differentially methylated regions (DMRs) were called using the Probe LASSO method in the ChAMP package and then annotated and displayed with the inner pipeline of Sinotech Genome Technology. Fractions of a priori known cell subtypes, including the immune cells present in the samples, were identified using the EpiDISH package (26).

Analysis of the TME by mIF

mIF staining was performed using an Akoya OPAL Polaris 7-Color Automation IHC (ImmunoHistoChemistry) Kit (NEL871001KT). formalin-fixed paraffin-embedded (FFPE) tissue slides were first deparaffinized in a BOND RX system (Leica Biosystems, Nussloch, Germany) and then incubated sequentially with primary antibodies targeting CD163 (Abcam, Cambridge, UK; ab182422, 1:500), CD68 (Abcam, ab213363, 1:1000), PD-1 (CST, Danvers, MA, USA; D4W2J, 86163S, 1:200), PD-L1 (CST, E1L3N, 13684S, 1:400), CD3 (Dako, Glostrup, Denmark; A0452), CD4 (Abcam, ab133616, 1:100), CD8 (Abcam, ab178089, 1:100), CD56 (Abcam, ab75813, 1:100), CD20 (Dako, L26, IR604), FOXP3 (Abcam, ab20034, 1:100), pan-CK (Abcam, ab7753, 1:100), and S100 (Abcam, ab52642, 1:200) (Akoya Biosciences, Marlborough, MA, USA). This was followed by incubation with secondary antibodies and the corresponding reactive Opal fluorophores. Nuclei were stained with 4’,6-diamidino-2-phenylindole (DAPI). Tissue slides that were bound with primary and secondary antibodies, but not fluorophores, were defined as negative controls to assess the autofluorescence (AF). Multiplex-stained slides were scanned using a Vectra Polaris Quantitative Pathology Imaging System (Akoya Biosciences) at 20 nm wavelength intervals from 440–780 nm with a fixed exposure time and an absolute magnification of ×200 (using a 20×/0.75 NA objective lens). All the scans on each slide were superimposed to obtain a single image. The multilayer images were imported into inForm v.2.4.8 (Akoya Biosciences) for quantitative image analysis. The tumor parenchyma and stroma were differentiated using an automated tissue segmentation algorithm based on pan-CK staining. Crucially, automated single-cell segmentation was performed based on nuclear DAPI staining to accurately identify and delineate individual cells. Following segmentation, cell phenotyping was conducted by assessing the expression of specific markers within the designated subcellular compartments of each individual cell. The quantities of various cell populations are strictly expressed as the absolute count of stained cells per square millimeter and as the percentage of positively stained cells among all nucleated cells.

Statistical analysis

Given the rarity of simultaneous primary SQ and SCLC and the limited sample size, this study was primarily exploratory and descriptive. Clinical characteristics, mutation profiles, mutation overlaps, immune cell infiltration, and survival outcomes were summarized descriptively. Bioinformatics analyses and visualization were performed using the methods described above.

Results

Patient characteristics and follow-up information

The clinical characteristics of these three patients are summarized in Table 1 and Figure 1. All the patients were male, which might be closely associated with the biological behavior of smoking in SQ and SCLC, as all three patients had a history of heavy smoking with at least 40 pack-years. Interestingly, all three patients with pathologically diagnosed SCLC had the peripheral type, not the classical central type. The two P1 tumors were located in different lobes: a pure SCLC of 1.7 cm with visceral pleura invasion in the left upper lobe (LUL) with lobectomy and a nonkeratinized SQ of 1.5 cm with visceral pleura invasion in the left lower lobe (LLL) with wedge resection. The tumors of the other two patients were both in the same lobe. The tumors for P2 were in the LUL with sleeve resection with a pure SCLC of 2.0 cm and an SQ of 3.5 cm. The tumors for P3 were in the LUL with left pneumonectomy resection with a pure SCLC of 1.8 cm and a keratinized SQ of 4.0 cm. As shown in Table 1, the lymphadenectases of P1 and P2 were both metastasized from SCLC, P1 with N2 metastasis for station 5 and N1 metastases for stations 10 and 11, P2 with N2 metastasis for station 4 and N1 metastasis for station 10. The lymphadenectases of P3 were metastasized from the SQ with N1 metastases for station 11 (the left superior hilar lymph node, left inferior hilar lymph node, and left upper and lower interlobar lymph nodes). With the last follow-up to October 2024, P1 had died by December 2021, P2 had died by April 2021, and P3 had died by June 2021. The OS times were 4.21, 3.37, and 2.87 years for P1, P2 and P3, respectively. Interestingly, the OS times were longer in P1 and P2, whose lymph nodes metastasized from N2 of SCLC, than in P3, whose lymph nodes metastasized from N1 of SQ.

Figure 1 Pathological and CT images of the three patients. Representative chest CT images and H&E-stained pathological images of the three patients, P1, P2, and P3. Pathological images were acquired at an original magnification of ×200. From left to right, the images show SCLC, SQ, and one representative metastatic lymph node. Red arrows indicate the corresponding tumor lesions or metastatic lymph nodes. (A) P1 is a 57-year-old male with 40 pack-years of smoking who presented with chest distress in September 2017. Chest CT on 09/20/2017 showed densities of the LUL and LLL with enlarged mediastinal lymph nodes. The final pathological results revealed SCLC measuring 1.7 cm with visceral pleural invasion of the LUL lobectomy with TTF1+, P40−, ChgA−, CK+, CD56+, CK5/6−, Syn+, and Ki67 (80%+) (P1_SCLC), and SQ measuring 1.5 cm with PL1-2 visceral pleural invasion of the LLL wedge resection with TTF1−, P40+, ChgA−, CK+, CD56−, CK5/6+, Syn-, and Ki67 (40%+) (P1_SQ). The lymph node dissection revealed SCLC metastases for stations 5 (P1_MLN1) and 10 (P1_MLN2) and left superior hilar lymph nodes and negative results for stations 6, 7, 8 and 11. (B) P2 is a 51-year-old male with 60 pack-years of smoking who presented with sudden hemoptysis in December 2017. Chest CT on 12/06/2017 showed two masses on the LUL with enlarged mediastinal lymph nodes. The final pathological results revealed SCLC measuring 2.0 cm in size during LUL sleeve resection with TTF1+, P40−, ChgA+, CK+, CD56+, CK5/6−, CK7−, Syn+, Ki67 (70%+), and CD117+ (P2_SCLC) and an SQ measuring 3.5 cm with PL0 visceral pleural invasion during LUL sleeve resection with TTF1−, P40+, ChgA−, CK+, CD56−, CK7−, CK5/6+, Syn−, Ki67 (60%+), and CD117(+) (P2_SQ). The lymph node dissection revealed SCLC metastases for stations 4 (P2_MLN1) and 10 (P2_MLN2) and negative results for stations 5, 6, 7, 9, 11, and 12. (C) P3 is a 47-year-old male with 75 pack-years of smoking who presented with bloody phlegm in May 2018. Chest CT on 06/15/2018 showed two masses on the LUL with enlarged mediastinal lymph nodes. The final pathological results revealed SCLC measuring 1.8 cm in size during left pneumonectomy resection with TTF1+, P40−, ChgA+, CK+, CD56+, CK5/6−, CK7+, Syn+, Ki67 (70% +), and NUT− (P3_SCLC), SQ measuring 4.0 cm with bronchial wall infiltration during left pneumonectomy resection with TTF1−, P40+, ChgA−, CK+, CD56−, CK7+, CK5/6+, Syn−, Ki67 (50% +), and NUT− (P3_SQ). The lymph node dissection revealed SQ metastases for station 11 with a left superior hilar lymph node (P3_MLN1) and 11 with a left inferior hilar lymph node (P3_MLN2) and negative results for stations 4, 5, 6, 7, 8, and 9. The degree of VPI in lung cancer can be divided into PL0, PL1 and PL2, which are defined as tumor growth in the parenchyma or incomplete penetration of the elastic layer (PL0), tumor invasion beyond the elastic layer (PL1) and tumor invasion on the pleural surface (PL2), respectively, by the IASLC (27). CT, computed tomography; H&E, hematoxylin and eosin; IASLC, International Association for the Study of Lung Cancer; LLL, left lower lobe; LUL, left upper lobe; SCLC, small-cell lung cancer; SQ, squamous cell carcinoma; VPI, visceral pleural invasion.

Mutations in the primary SQ and SCLC of each patient revealed obvious mutual exclusion and a clear evolutionary relationship with the corresponding lymphadenectases

The detailed information of the mutations in these three patients is summarized in table available at https://cdn.amegroups.cn/static/public/tlcr-2025-1-1446-1.xlsx. All the mutations according to the chromosome locations and alternative alleles were compared in the primary SQ, SCLC and corresponding lymphadenectases in each patient. In these three patients, the mutations in primary SCLC rarely overlapped with those in primary SQ in the same patient. The numbers of overlapping mutations of the primary SQ and SCLC in these three patients were 1, 0 and 1, respectively (Figure 2). According to the data for the three patients, there were more somatic mutations in primary SCLC samples than in SQ samples (average mutation number 240.67 vs. 134.33) (Figure S1). Not surprisingly, the somatic mutations in the MLNs of each patient were consistent with those of the corresponding original primary lung tumor, which was pathologically confirmed. The somatic mutations in the two lymph nodes metastasized from SCLC in patients P1 and P2 significantly overlapped with those in primary SCLC of the lung, and the somatic mutations in the two MLNs derived from SQ in patient P3 similarly significantly overlapped with those in primary SQ of the lung (Figure 2).

Figure 2 Basic information of the tumors and the relationships between tumors based on somatic mutations for each patient. (A) The four figures, listed from left to right, provide information on the primary tumor and metastatic lymph nodes of P1, including the basic clinical information and the approximate location and proportional size of the two primary tumors in the lungs, the evolutionary relationship between the primary lung and metastatic lymph nodes predicted from the perspective of gene mutations, and the comparison of mutations in two metastatic lymph nodes with primary lung cancer as Venn diagrams. The schematic illustration of the lung was generated using BioGDP (https://biogdp.com/). (B,C) The corresponding results for P2 and P3. The circle in green represents SQ, and the circle in yellow represents SCLC. LLL, left lower lobe; LUL, left upper lobe; SCLC, small-cell lung cancer; SQ, squamous cell carcinoma.

Based on the mutations detected in the primary SQ, SCLC and corresponding lymphoma samples, we further predicted potential cancer evolution using the MesKit package. Not surprisingly, primary SQ and SCLC in patients P1, P2 and P3 were mutually exclusive and could be distinguished according to their respective mutations; moreover, the mutations in MLNs were highly evolutionarily consistent with the corresponding original primary tumors (Figure 2).

Gene-level analysis confirmed the mutual exclusion of mutations in SQ and SCLC and the functional characteristics of genes mutated in the lymphadenectases

The detailed mutated genes and mutation sites of different genes in the SQ, SCLC and lymphadenectase tissues of the three patients were summarized and analyzed (table available at https://cdn.amegroups.cn/static/public/tlcr-2025-1-1446-1.xlsx). Combined analysis of all the mutated genes in the SQ, SCLC and lymphadenectase tissues revealed that TP53, RB1, AXIN1, FAT1, LCK and LRP1B were the most frequently mutated genes, as determined by a focus on the cancer-related genes recorded in the Catalogue of Somatic Mutations in Cancer (COSMIC) dataset (Figure 3A). Undoubtedly, the mutated genes in MLNs were highly evolutionarily consistent with those in the corresponding primary tumors (Figure 3A). Notably, TP53 was found to be mutated in all three primary SQs, and RB1 was found to be mutated in all three primary SCLCs (Figure 3A, Figure S2), which corresponded to previous studies of SQ (28) and SCLC (7,29), respectively, and the Rb-P53 axis plays a role in the tumorigenesis of neuroendocrine lung cancer (30).

Figure 3 Mutation-based relationships between tumors compared at the gene level for each patient. (A) CoMut plots derived from twelve tissues from the three patients, including two primary lung tumors and metastatic lymph nodes, illustrating the landscape of high-frequency tumor-associated genes across these tissues. Each row represents a gene, and each column corresponds to a sample in the main plot. The legend indicates the mutation types represented by different colors, and the bottom panel displays the sample types. (B-D) Detailed mutation site information of TP53/LRP1B in SQ and SCLC of the same patient and detailed information on the protein domains and the specific amino acid alterations caused by mutations are presented in the form of lollipop plots for P1, P2 and P3. SCLC, small-cell lung cancer; SQ, squamous cell carcinoma.

Interestingly, the same mutated genes with different mutation sites were detected in both the primary SQ and SCLC of the lung in the same patient. TP53 was found to be mutated in both the primary lung SQ and SCLC in patient P1 (Figure 3B). LRP1B was mutated to p.E4091D in the primary lung SQ, and multiple mutations (p.G830W, p.G830V, p.I1195V, p.H1972N and p.A2455D) were detected in the SCLC of patient P2 (Figure 3C). Similarly, TP53 was also found to be mutated in both the primary SQ and SCLC in patient P3, with p.R273L in SQ and p.H179R in SCLC (Figure 3D). This phenomenon of some of the same mutated genes but different mutation sites further demonstrates genomic independence but with some complex and complicated relationships with the primary SQ and SCLC of the lungs in these patients.

An analysis of the genes and mutations of the MLNs compared with those of the primary lung tumors revealed that a total of 441 (35.6%) mutations and 436 (39.3%) genes overlapped in both the MLNs and primary lung tumors at the mutation and gene levels, respectively, according to the combined joint analysis of these three patients (Figure 4A). Interestingly, these 436 genes were enriched in mainly extracellular matrix (ECM) organization-related GO terms (Figure 4B,4C), which may be involved in tumor cell metastasis. Although 85 genes that were uniquely mutated in lymphadenectases presented no enriched pathways (Figure 4D), the genes encoding CARD11 and NOTCH2 were relatively central to the interaction network according to the STRING database. The genes CARD11 (31-33) and NOTCH2 (34-36) are reportedly related to tumor-infiltrating immune cells, and Notch signaling-related genes, including the Notch receptor NOTCH1/2/3 and the ligands JAG2 and DLL4, are related to neuroendocrine (NE) growth and NE transformation in SCLC (37).

Figure 4 Comparison of mutations between primary lung tumors and metastatic lymph nodes and the characteristics of mutated genes in metastatic lymph nodes. (A) Mutations in all primary lung tumors, including SQ and SCLC, and all metastatic lymph nodes are summarized and compared. The upper figure shows the overlap at the specific mutation level between primary lung and metastatic lymph nodes presented as a Venn diagram, and the lower Figure displays the comparison at the gene level. (B) GO enrichment results (top 30 GO terms) of the genes that were mutated in both the primary lung and metastatic lymph nodes. The different sizes of each circle represent the number of genes enriched in the corresponding GO term, and the different colors indicate the type of GO module, including BP, MF, and CC. The plot was generated using the R package ClusterProfiler, with enrichment thresholds set at P value cutoff =0.05 and q value cutoff =0.1. (C) The GO terms listed in Figure B are shown based on the number of genes shared with each other. The different sizes of each circle represent the number of genes enriched in the corresponding GO term, and the different colors indicate the type of GO module, including BP, MF or CC. The different thicknesses of the lines represent the number of overlapping genes between the connected GO terms. (D) Network diagram of 85 genes mutated specifically in metastatic lymph nodes, which was generated using the STRING database (online at https://string-db.org/). The different colored lines connect the proteins encoded by these genes, with the legend below indicating the specific types of protein-protein interactions represented by each line color. BP, biological process; CC, cellular component; GO, Gene Ontology; MF, molecular function; SCLC, small-cell lung cancer; SQ, squamous cell carcinoma.

Characterization of the TME in primary tumors and MLNs using DNA methylation and mIF profiling

Primary SQ and SCLC tumors presented mutually independent mutation statuses, as described above; however, genomic data did not reveal the potential reasons for longer survival in patients P1 and P2, whose lymph nodes metastasized from N2 of SCLC, which is inconsistent with the prognosis of traditional central SCLC and cannot be explained by conventional genomic understanding (14). Considering this point, we applied two more omics technologies, DNA methylation profiling and mIF, to further investigate the potential epigenetic and TME characteristics of SQ and SCLC tissues. The DMRs in primary SQs and primary SCLCs from these three patients were investigated in comparisons with normal tissues, and the results revealed that these DMRs presented many common and specific characteristics between primary SQs and SCLCs from the lung. The DMRs of the primary SQs in the three patients are shown in Figure S3A according to chromosome location, and the DMRs of the primary SCLCs are shown in Figure S3B. GO enrichment analysis was further performed for the DMR-related genes. Figure S4A shows the overlapping DMR-related genes between the SQ and SCLC samples. The enrichment results of the specific genes of primary SQ and SCLC (Figure S4B,S4C) and the common genes (Figure S4D) are shown in Figure S4B-S4D, respectively. Interestingly, DMR-related genes in SCLC samples were significantly enriched in cell adhesion-related GO terms.

We then investigated the TME status in SQ, SCLC and lymphadenectases. First, we identified the fractions of a priori known cell subtypes, including the immune cells present in the samples, with the EpiDISH package using DNA methylation array data. The fractions of cell types, including epithelial cells and immune cells, did not show obvious differences (Figure 5A), whereas there were more immune cells in the MLNs than in the primary lung tumors (SCLC or SQ) of the three patients, including B cells (20.96% vs. 6.28%), CD4+ (29.05% vs. 14.49%) and CD8+ (7.30% vs. 0%) T cells (Figure 5A), as determined by calculating the mean proportions. Interestingly, there was greater infiltration of immune cells, including B cells (28.53% vs. 5.83%) and CD4+ T cells (34.68% vs. 17.81%), into the MLNs in P1 and P2 than in those in P3 (Figure 5A).

Figure 5 Tumor microenvironment characteristics based on the methylation profile and mIF detection. (A) Fractions of a priori known cell subtypes, including the presence of immune cells in each tumor, as predicted by the EpiDISH package based on methylation array data. (B) Percentages of B cells, NK cells, CD4+ T cells and CD8+ T cells around tumor cells in each tumor type as predicted by mIF. (C) Representative regions of B cells, NK cells and CD4+ T cells in each tumor according to mIF. DAPI was used to stain all the cells, and the CK marker was specifically expressed in the tumor cells. The white bar in the bottom left corner represents 100 μm. DAPI, 4’,6-diamidino-2-phenylindole; mIF, multiplex immunofluorescence; NK, natural killer.

To further validate the TME status, we detected the immune microenvironments in these three patients with primary SQ, primary SCLC, and one of the MLNs, MLN2, to directly evaluate the overall TIL count using mIF and to predictively evaluate immune cells, including B cells, CD4+ T cells, CD8+ T cells and NK cells, using DNA methylation data. The levels of immune infiltration were similar, and both were very low in primary SQ and SCLC (Figure 5B), whereas there were more B cells in MLN2 of P1 and P2 than in primary lung tumors (SQ and SCLC) (6.77% vs. 1.20%), as determined by calculating the mean proportions (Figure 5B), and more CD4+ T cells in P1 than in primary lung tumors (13.22% vs. 1.68%) (Figure 5B). The levels of immune infiltration were very low in the lymph nodes of P3. Figure 5C shows representative regions of B cells, NK cells and CD4+ T cells in each tumor by mIF, and Figure S5 shows representative regions of CD8+ T cells.

Collectively, while DNA methylation profiling indicated a trend of increased immune involvement in MLNs, mIF analysis provided a definitive characterization of the immune landscape. The mIF quantification revealed distinct immune phenotypes within the MLNs among the patients: (I) overall, immune cell infiltration was higher in the MLNs compared to their paired primary tumors (SQ and SCLC); (II) the immune composition within the SCLC MLNs exhibited heterogeneity: the MLN of Patient 1 displayed a ‘dual-infiltrated’ phenotype characterized by high densities of both T cells (CD4+) and B cells, whereas the MLN of Patient 2 presented a ‘B-cell dominant’ phenotype with substantial B-cell enrichment despite a paucity of T cells; (III) in contrast, the MLN of Patient 3 (SQ) exhibited an ‘immune-desert’ phenotype with minimal presence of both T and B lymphocyte lineages.

Discussion

In the present study, we systematically studied the genetic and epigenetic landscape and performed an in-depth TME analysis of three patients with MPLC with rare simultaneous primary SQ and SCLC, including the clinical features and somatic mutation characteristics. The molecular characterization confirmed the origin of each independent tumor and MLN from a genetic perspective and revealed that TP53 plays a role in SQ-SCLC development in heavy smokers. DNA methylation profiling and multiplex mIF revealed obvious immune infiltration in lymphadenectases compared with primary lung tumors, and interestingly, more immune cells from metastatic N2 lymph nodes from SCLC in P1 and P2 had better OS than those from SQ in P3, with only metastatic N1 lymph nodes, suggesting that the accumulation of epigenetic machinery modulates immune cell functions and the antitumor immune response.

Treatment strategies for lung cancer are now moving from a single pathology-based approach to a comprehensive genetic profile and immune markers. Chemotherapy is the mainstay of treatment for both NSCLC and SCLC, but the drugs used are distinct for each different pathological type, and treatment strategies differ substantially from one type to another. The classification of these tumors with simultaneous primary SQ and SCLC was accurately defined by CT imaging and pathological images and IHC markers by two independent pathologists. Considering that additional molecular biological profiles provide more detailed information on the different pathological types and the identified sources of MLNs, we performed WES to detect genomic mutations at the whole-gene level.

The somatic mutation landscape, including the mutated genes and mutation sites, showed distinct features between primary SQ and SCLC of each patient, and interestingly, almost no overlapping somatic mutations among these two different primary tumor types were detected. Notably, the mutated genes and mutation sites of the same gene, including TP53 and LRP1B, were different between primary SQ and SCLC, which is consistent with previously reported studies on somatic mutations in MPLC (3,38). These genes may have similar roles in the pathogenesis and progression of simultaneous SQ and SCLC but via different mutation sites. The frequency of TP53 mutations in SCLC is between 75% and 90%, indicating that the loss of TP53 is an important event in the onset of SCLC development. LRP1B is frequently mutated in lung cancer and is related to the TMB and the outcome of patients treated with immune checkpoint blockade (39-42). In recent years, TMB has been gradually used in the prediction of PD-1/PD-L1 immunotherapy and drug indications in lung cancer (43-45). We further demonstrated that the number of somatic mutations in SCLC was greater than that in SQ (average mutation number: 240.67 vs. 134.33), suggesting that TMB in SCLC was greater than that in SQ in this particular type of MPLC. Therefore, epigenetic analysis and multiplex mIF were further used to study the TME of these two independent types of tumors to better understand immune cell infiltration.

In our study, the mutations in the MLNs significantly corresponded to the original primary cancer. A total of 436 genes overlapped in both MLNs and corresponding primary lung tumors according to the combined analysis of these three patients, and these genes that were mutated in the lymphadenopathies were enriched mainly in ECM organization-related GO terms and cell adhesion-related functional terms. ECM organization has been found to be related to lymph node metastases in multiple types of cancers, including gastric cancer (46), prostate cancer (47) and lung cancer (48). In NSCLC, Su Bin LIM et al. developed a 29-gene ECM-related prognostic and predictive indicator that was an independent predictor of survival outcome (48).

Interestingly, according to the Search Tool for the Retrieval of Interacting Genes and Genomes (STRING) database, 85 genes, such as those encoding CARD11 and NOTCH2, which are related to tumor-infiltrated immune cells, were uniquely mutated in lymphadenectases. CARD11, a member of the caspase recruitment domain (CARD) family, also known as CARMA1, is expressed mainly in lymphoid tissues and is involved in both adaptive immunity (31,32) and carcinogenesis (49). CARD11 mutation could allow T-cell activation and result in Th2-biased inflammatory disease (50), which is related to increased numbers of tumor-infiltrated immune cells in patients with melanoma (33). NOTCH2 has been shown to participate in the progression of lung cancer and to be a potential therapeutic target (51,52) and present in immune infiltrate (34-36); more importantly, NOTCH2-based Notch signaling is related to SCLC development (37). These findings reveal the higher-level architecture that assembles individual tolerance mechanisms together to achieve strong resistance to autoimmunity. Hence, it will be interesting in future studies to determine the nature and immunogenicity of the “driver” autoantigens recognized by CD8+ and CD4+ T cells in CARD11 and NOTCH2 mutations, providing a framework for understanding the latency and heterogeneity in clinical autoimmune disease and the complexity of its inheritance.

We hypothesize that immune infiltration in cancer may also be defined by changes in certain epigenetic signatures (12). DNA methylation profiling revealed overlapping and unique DMRs in SQ and SCLC and predicted a similar TME in SQ and SCLC, which was verified by mIF, indicating that there is a similar balance between the TMEs of the two primary tumors in patients with this type of lung cancer. DNA methylation prediction and mIF further demonstrated obvious immune infiltration in metastatic lymphadenectases compared with that in primary lung tumors. Surprisingly, compared to P3 with SQ of metastatic N1, P1 and P2 with SCLC of metastatic N2 had greater infiltration of immune cells, including B cells (28.53% vs. 5.83%) and CD4+ T cells (34.68% vs. 17.81%), resulting in better survival, which may benefit from the enhanced immune activity in the lymph nodes of P1 and P2, as described above.

Because pathology and TNM stage are the most important independent prognostic factors in SCLC and NSCLC (53), patients with metastatic N1 lymph nodes have better survival than those with N2 involvement (54), and patients with SQ have better survival than those with SCLC when the same tumor stage is considered. A cohort study of 205 patients with SCLC, 79 of whom were stage I and 42 of whom were stage II, revealed that higher percentages of T cells (CD3 and CD45) and B cells (CD20) were correlated with better survival, suggesting a potential role of enhanced immune activity in resected SCLC (55). Interestingly, the survival of patients with resected stage II SCLC in this study was similar to that of patients with stage I SCLC, suggesting that extending surgery to patients with hilar lymph node involvement may be beneficial for enhancing the body’s immune system (55). The prognostic significance of the immunological TME was supported by our study. A similar report from the Mayo Clinic reported that the 3-year survival of patients with stage III SCLC who underwent surgery was 71% (56). Similar better OS data for patients with stage III SCLC postsurgery were reported in a Japanese study (57), suggesting a potential positive influence among Asian patients. Another 135 patients with extensive-stage SCLC demonstrated that a durable clinical benefit of immunotherapy could be observed in patients with CD8+ T-cell infiltration and a high neoantigen load (58). These findings may be one of the reasons for the relatively poor prognosis of P3.

A comprehensive comparative analysis of the genome and TME of SCLC by gene expression profiling and TME analyses revealed that increased numbers of TILs were the key determinants for the long-term survival of patients with SCLC, indicating a microenvironment with less tumor inhibition (17). Higher numbers of CD45+ or CD8+ T cells were associated with significantly better OS in patients who undergo SCLC surgery, especially in the two patients with high levels of CD45+ or CD8+ T cells who survived more than four years (59). Another similar study confirmed that more tumor-infiltrating CD45+ immune cells were associated with a more favorable prognosis in patients with SCLC, including a group of four long-term survivors who lived for longer than four years (60). Building on these observations, our mIF analysis notably revealed that the MLN of P2 presented a unique ‘B-cell dominant’ phenotype, characterized by substantial B-cell enrichment despite a paucity of T cells. While previous research has primarily focused on T-cell-mediated immunity, accumulating evidence indicates that B-cell infiltration can independently represent a significant degree of immune infiltration intensity and actively orchestrate the anti-tumor response. For instance, Petitprez et al. highlighted that B-cell enrichment is strongly associated with prolonged survival and improved immunotherapy outcomes (61), and Kinoshita et al. further reported a positive correlation between tumor-infiltrating B cells and favorable prognosis in resected lung cancer (62). Consequently, the substantial B-cell infiltration observed in P2 offers a plausible explanation for the enhanced local immune activity and relatively prolonged survival in this patient, underscoring the importance of evaluating the diverse landscape of TILs beyond T cells.

This striking observation was very rare and raised several unsettled clinical issues worthy of consideration. What is the best treatment mode for this type of limited, early-stage, combined histological tumor, such as those in our patients with combined SQ and SCLC? How to select patients with early-stage SCLC with immune infiltration who could benefit from surgery and subsequent treatment may require more predictive markers to help clinicians make decisions. Similarly, a 65-year-old Japanese man with primary collision cancer of resectable stage IIIA (P-T2N2M0) SQ and stage IA (P-T1N0M0) SCLC of the right lower lobe was reported in 2006 (4), but no additional genetic analysis was performed at that time to provide guidance for subsequent treatment. Our research revealed that this type of peripheral SCLC may differ from traditional central SCLC, which has a relatively good prognosis with increased immune infiltration.

This study has several limitations. First, the number of patients in our study was relatively small and needs to be validated in an independent cohort. However, because similar cases of simultaneous operable cancers with SQ and SCLC are rare, especially with long-term survival, which is infrequent among patients with SCLC, finding validation cohorts is challenging. Second, it is difficult to estimate the potential interactions between surgery and antitumor immunity in our study because all our analyses were based on surgical samples. Additional studies investigating biopsy samples from patients who received antitumor treatment, especially immunotherapy, would be helpful to address this limitation. Third, only OS data were obtained, without disease-free survival (DFS) data. A second biopsy can provide more valuable information and tumor evolution data if it is performed when the disease has relapsed to determine which is more predominant in the cancer development.

The multiomics findings of this study hold significant clinical importance for the diagnosis and treatment of such rare patients. First, for patients with suspected multiple primary tumors, it is crucial not to treat their tumors as metastatic cancers. Biopsy of all suspected tumors is recommended to determine their pathological type. Second, our results show that multiple primary tumors within the same patient present entirely different mutation profiles and evolutionary characteristics. Therefore, when designing personalized treatment plans for these patients, it is advisable to comprehensively consider genetic mutations across all primary tumors and adopt a rational combined treatment strategy. Third, our study revealed immune infiltration patterns in both primary and lymph node metastatic tumors in these patients, suggesting that perioperative neoadjuvant immunotherapy followed by further assessment of surgical feasibility could be considered.

In summary, for the first time, we studied the mutational and epigenetic landscape accompanied by the TME characteristics of three patients with special rare types of MPLC with SQ and SCLC and found that more immune cell infiltration in metastatic lymphadenectases from SCLC was associated with better OS. The resulting findings and conclusions may benefit the precise diagnosis and treatment of this type of lung cancer.

Conclusions

This study provides critical multiomics insights into the molecular landscape and TME of patients with simultaneous primary SQ and SCLC, a rare form of MPLC. Our findings underscore the importance of distinguishing multiple primary tumors from metastatic disease through comprehensive pathological and genetic assessment. Whole-exome sequencing revealed entirely distinct mutational profiles, highlighting the need for personalized treatment strategies that account for all primary tumors. Additionally, immune profiling identified unique infiltration patterns in both primary and lymph node lesions, suggesting the potential benefit of perioperative neoadjuvant immunotherapy. These insights offer a foundation for precision oncology, advocating a tailored, multi-targeted therapeutic approach to optimize clinical outcomes for these complex lung cancer cases. However, given the limited sample size of the current study, further validation with larger patient cohorts is necessary to verify and corroborate these observations.

Acknowledgments

We thank Fengcai Wen, Ding Zhang, and Xinyue Sun from the Medical Department, 3D Medicines Inc., Shanghai, China for their assistance and guidance with the immunofluorescence experiments in this research.

Footnote

Data Sharing Statement: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-1-1446/dss

Peer Review File: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-1-1446/prf

Funding: This study was supported by Translational Medicine National Major Science and Technology Infrastructure (Shanghai) (TMSK-2024-116), Shanghai Municipal Health Commission Medical New Technology Research and Transformation Seed Program (2024ZZ2027), Shanghai Municipal Medical and Health Outstanding Academic Leader Program (GWVI-11.2-XD40), Interdisciplinary Program of Shanghai Jiao Tong University (YG2025LC10), Shanghai Rising-Star Program (23QA1408500), and Young Talents Program of Shanghai Municipal Health Commission (2022YQ061).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-1-1446/coif). The authors have no 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 single-center study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Institutional Review Board (IRB) of Shanghai Chest Hospital (ethics approval No. KS21019) and informed consent was obtained from all individual participants.

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/.

References

1. Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2024;74:229-63. [Crossref] [PubMed]
2. Chen C, Huang X, Peng M, et al. Multiple primary lung cancer: a rising challenge. J Thorac Dis 2019;11:S523-36. [Crossref] [PubMed]
3. Chen M, Ma H, Yu H, et al. Genomic heterogeneity of multifocal NSCLC. J Clin Oncol 2020;38:e21595.
4. Sugano M, Kawashima O, Nagashima T, et al. Collision cancer of squamous cell carcinoma and small cell carcinoma of the lung; report of a case. Kyobu Geka 2006;59:497-500.
5. Suzuki S, Ohtsuka T, Hato T, et al. Primary pulmonary collision tumor with three components in the underlying interstitial lung disease. Thorac Cancer 2014;5:460-2. [Crossref] [PubMed]
6. Varghese AM, Zakowski MF, Yu HA, et al. Small-cell lung cancers in patients who never smoked cigarettes. J Thorac Oncol 2014;9:892-6. [Crossref] [PubMed]
7. George J, Lim JS, Jang SJ, et al. Comprehensive genomic profiles of small cell lung cancer. Nature 2015;524:47-53. [Crossref] [PubMed]
8. Peifer M, Fernández-Cuesta L, Sos ML, et al. Integrative genome analyses identify key somatic driver mutations of small-cell lung cancer. Nat Genet 2012;44:1104-10.
9. Rudin CM, Durinck S, Stawiski EW, et al. Comprehensive genomic analysis identifies SOX2 as a frequently amplified gene in small-cell lung cancer. Nat Genet 2012;44:1111-6. [Crossref] [PubMed]
10. Canale M, Urbini M, Petracci E, et al. Genomic Profiling of Extensive Stage Small-Cell Lung Cancer Patients Identifies Molecular Factors Associated with Survival. Lung Cancer (Auckl) 2025;16:11-23. [Crossref] [PubMed]
11. Campbell JD, Alexandrov A, Kim J, et al. Distinct patterns of somatic genome alterations in lung adenocarcinomas and squamous cell carcinomas. Nat Genet 2016;48:607-16. [Crossref] [PubMed]
12. Bacolod MD, Barany F, Fisher PB. Can CpG methylation serve as surrogate markers for immune infiltration in cancer? Adv Cancer Res 2019;143:351-84. [Crossref] [PubMed]
13. He R, Feng X, Yang K, et al. Construction of a 5-methylcytosine-Related Molecular Signature to Inform the Prognosis and Immunotherapy of Lung Squamous Cell Carcinoma. Expert Rev Mol Diagn 2022;22:905-13. [Crossref] [PubMed]
14. Luo H, Shan J, Zhang H, et al. Targeting the epigenetic processes to enhance antitumor immunity in small cell lung cancer. Semin Cancer Biol 2022;86:960-70.
15. Jiang T, Shi J, Dong Z, et al. Genomic landscape and its correlations with tumor mutational burden, PD-L1 expression, and immune cells infiltration in Chinese lung squamous cell carcinoma. J Hematol Oncol 2019;12:75. [Crossref] [PubMed]
16. Thomas PL, Groves SM, Zhang YK, et al. Beyond Programmed Death-Ligand 1: B7-H6 Emerges as a Potential Immunotherapy Target in SCLC. J Thorac Oncol 2021;16:1211-23. [Crossref] [PubMed]
17. Muppa P, Parrilha Terra SBS, Sharma A, et al. Immune Cell Infiltration May Be a Key Determinant of Long-Term Survival in Small Cell Lung Cancer. J Thorac Oncol 2019;14:1286-95. [Crossref] [PubMed]
18. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 2014;30:2114-20. [Crossref] [PubMed]
19. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 2009;25:1754-60. [Crossref] [PubMed]
20. McKenna A, Hanna M, Banks E, et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res 2010;20:1297-303. [Crossref] [PubMed]
21. McLaren W, Gil L, Hunt SE, et al. The Ensembl Variant Effect Predictor. Genome Biol 2016;17:122. [Crossref] [PubMed]
22. Chen C, Chen H, Zhang Y, et al. TBtools: An Integrative Toolkit Developed for Interactive Analyses of Big Biological Data. Mol Plant 2020;13:1194-202. [Crossref] [PubMed]
23. Mayakonda A, Lin DC, Assenov Y, et al. Maftools: efficient and comprehensive analysis of somatic variants in cancer. Genome Res 2018;28:1747-56. [Crossref] [PubMed]
24. Liu M, Chen J, Wang X, et al. MesKit: a tool kit for dissecting cancer evolution of multi-region tumor biopsies through somatic alterations. Gigascience 2021;10:giab036. [Crossref] [PubMed]
25. Wu T, Hu E, Xu S, et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innovation (Camb) 2021;2:100141. [Crossref] [PubMed]
26. Teschendorff AE, Breeze CE, Zheng SC, et al. A comparison of reference-based algorithms for correcting cell-type heterogeneity in Epigenome-Wide Association Studies. BMC Bioinformatics 2017;18:105. [Crossref] [PubMed]
27. Goldstraw P, Chansky K, Crowley J, et al. The IASLC Lung Cancer Staging Project: Proposals for Revision of the TNM Stage Groupings in the Forthcoming (Eighth) Edition of the TNM Classification for Lung Cancer. J Thorac Oncol 2016;11:39-51. [Crossref] [PubMed]
28. Comprehensive genomic characterization of squamous cell lung cancers. Nature 2012;489:519-25.
29. Rudin CM, Brambilla E, Faivre-Finn C, et al. Small-cell lung cancer. Nat Rev Dis Primers 2021;7:3. [Crossref] [PubMed]
30. Meder L, König K, Ozretić L, et al. NOTCH, ASCL1, p53 and RB alterations define an alternative pathway driving neuroendocrine and small cell lung carcinomas. Int J Cancer 2016;138:927-38. [Crossref] [PubMed]
31. Gao J, Huo S, Zhang Y, et al. Construction of ovarian metastasis-related immune signature predicting prognosis of gastric cancer patients. Cancer Med 2023;12:913-29. [Crossref] [PubMed]
32. Shi X, Xia S, Chu Y, et al. CARD11 is a prognostic biomarker and correlated with immune infiltrates in uveal melanoma. PLoS One 2021;16:e0255293. [Crossref] [PubMed]
33. Zhao L, Luo T, Jiang J, et al. Eight gene mutation-based polygenic hazard score as a potential predictor for immune checkpoint inhibitor therapy outcome in metastatic melanoma. Front Mol Biosci 2022;9:1001792. [Crossref] [PubMed]
34. Cheung PF, Neff F, Neander C, et al. Notch-Induced Myeloid Reprogramming in Spontaneous Pancreatic Ductal Adenocarcinoma by Dual Genetic Targeting. Cancer Res 2018;78:4997-5010. [Crossref] [PubMed]
35. Li Z, Li X, Jin M, et al. Identification of potential biomarkers and their correlation with immune infiltration cells in schizophrenia using combinative bioinformatics strategy. Psychiatry Res 2022;314:114658. [Crossref] [PubMed]
36. Wang L, Yu S, Chan ER, et al. Notch-Regulated Dendritic Cells Restrain Inflammation-Associated Colorectal Carcinogenesis. Cancer Immunol Res 2021;9:348-61. [Crossref] [PubMed]
37. Quintanal-Villalonga A, Taniguchi H, Zhan YA, et al. Multiomic Analysis of Lung Tumors Defines Pathways Activated in Neuroendocrine Transformation. Cancer Discov 2021;11:3028-47. [Crossref] [PubMed]
38. Takamochi K, Oh S, Suzuki K. Combined mutation pattern analyses of epidermal growth factor receptor and K-ras genes for the differential diagnosis of primary versus metastatic lung adenocarcinomas. J Clin Oncol 2010;28:e21010.
39. Chen H, Chong W, Wu Q, et al. Association of LRP1B Mutation With Tumor Mutation Burden and Outcomes in Melanoma and Non-small Cell Lung Cancer Patients Treated With Immune Check-Point Blockades. Front Immunol 2019;10:1113. [Crossref] [PubMed]
40. Lan S, Li H, Liu Y, et al. Somatic mutation of LRP1B is associated with tumor mutational burden in patients with lung cancer. Lung Cancer 2019;132:154-6. [Crossref] [PubMed]
41. Zhu J, Tucker MD, Kao C, et al. Immune checkpoint inhibitor response in tumors with LRP1B variants. J Clin Oncol 2019;37:e14291.
42. Armstrong AJ, Dietz H, Balli D, et al. LRP1b Loss Predicts Sensitivity to Immunotherapy in Patients with NSCLC: An Analysis of the Phase III CheckMate-026 Randomized Trial. Clin Cancer Res 2025;31:5198-210. [Crossref] [PubMed]
43. Greillier L, Tomasini P, Barlesi F. The clinical utility of tumor mutational burden in non-small cell lung cancer. Transl Lung Cancer Res 2018;7:639-46. [Crossref] [PubMed]
44. Willis C, Fiander M, Tran D, et al. Tumor mutational burden in lung cancer: a systematic literature review. Oncotarget 2019;10:6604-22. [Crossref] [PubMed]
45. Fang J, Li Q, Xu N, et al. Systemic inflammation biomarkers can identify high tumor mutation burden in lung adenocarcinoma. BMC Cancer 2025;25:1543. [Crossref] [PubMed]
46. Moreira AM, Pereira J, Melo S, et al. The Extracellular Matrix: An Accomplice in Gastric Cancer Development and Progression. Cells 2020;9:394. [Crossref] [PubMed]
47. Luthold C, Hallal T, Labbé DP, et al. The Extracellular Matrix Stiffening: A Trigger of Prostate Cancer Progression and Castration Resistance? Cancers (Basel) 2022;14:2887. [Crossref] [PubMed]
48. Lim SB, Tan SJ, Lim WT, et al. An extracellular matrix-related prognostic and predictive indicator for early-stage non-small cell lung cancer. Nat Commun 2017;8:1734. [Crossref] [PubMed]
49. Bedsaul JR, Carter NM, Deibel KE, et al. Mechanisms of Regulated and Dysregulated CARD11 Signaling in Adaptive Immunity and Disease. Front Immunol 2018;9:2105. [Crossref] [PubMed]
50. Teh CE, Daley SR, Enders A, et al. T-cell regulation by casitas B-lineage lymphoma (Cblb) is a critical failsafe against autoimmune disease due to autoimmune regulator (Aire) deficiency. Proc Natl Acad Sci U S A 2010;107:14709-14. [Crossref] [PubMed]
51. Pietanza MC, Byers LA, Minna JD, et al. Small cell lung cancer: will recent progress lead to improved outcomes? Clin Cancer Res 2015;21:2244-55. [Crossref] [PubMed]
52. Yen WC, Fischer MM, Axelrod F, et al. Targeting Notch signaling with a Notch2/Notch3 antagonist (tarextumab) inhibits tumor growth and decreases tumor-initiating cell frequency. Clin Cancer Res 2015;21:2084-95. [Crossref] [PubMed]
53. Hwang JK, Page BJ, Flynn D, et al. Validation of the Eighth Edition TNM Lung Cancer Staging System. J Thorac Oncol 2020;15:649-54.
54. Rami-Porta R, Nishimura KK, Giroux DJ, et al. The International Association for the Study of Lung Cancer Lung Cancer Staging Project: Proposals for Revision of the TNM Stage Groups in the Forthcoming (Ninth) Edition of the TNM Classification for Lung Cancer. J Thorac Oncol 2024;19:1007-27. [Crossref] [PubMed]
55. Zhao X, Kallakury B, Chahine JJ, et al. Surgical Resection of SCLC: Prognostic Factors and the Tumor Microenvironment. J Thorac Oncol 2019;14:914-23. [Crossref] [PubMed]
56. Stish BJ, Hallemeier CL, Olivier KR, et al. Long-Term Outcomes and Patterns of Failure After Surgical Resection of Small-Cell Lung Cancer. Clin Lung Cancer 2015;16:e67-73. [Crossref] [PubMed]
57. Takei H, Kondo H, Miyaoka E, et al. Surgery for small cell lung cancer: a retrospective analysis of 243 patients from Japanese Lung Cancer Registry in 2004. J Thorac Oncol 2014;9:1140-5. [Crossref] [PubMed]
58. Kanemura H, Hayashi H, Tomida S, et al. The Tumor Immune Microenvironment and Frameshift Neoantigen Load Determine Response to PD-L1 Blockade in Extensive-Stage SCLC. JTO Clin Res Rep 2022;3:100373. [Crossref] [PubMed]
59. Eerola AK, Soini Y, Pääkkö P. A high number of tumor-infiltrating lymphocytes are associated with a small tumor size, low tumor stage, and a favorable prognosis in operated small cell lung carcinoma. Clin Cancer Res 2000;6:1875-81.
60. Wang W, Hodkinson P, McLaren F, et al. Histologic assessment of tumor-associated CD45(+) cell numbers is an independent predictor of prognosis in small cell lung cancer. Chest 2013;143:146-51. [Crossref] [PubMed]
61. Petitprez F, de Reyniès A, Keung EZ, et al. B cells are associated with survival and immunotherapy response in sarcoma. Nature 2020;577:556-60. [Crossref] [PubMed]
62. Kinoshita T, Muramatsu R, Fujita T, et al. Prognostic value of tumor-infiltrating lymphocytes differs depending on histological type and smoking habit in completely resected non-small-cell lung cancer. Ann Oncol 2016;27:2117-23. [Crossref] [PubMed]