Circulating tumor cells are associated with lung cancer subtypes: a large-scale retrospective study
Original Article

Circulating tumor cells are associated with lung cancer subtypes: a large-scale retrospective study

Lei Zhu1# ORCID logo, Erji Gao1#, Yusuke Tomita2, An Li3*, Bo Tao1*

1Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China; 2Department of Respiratory Medicine, Kumamoto University Hospital, Kumamoto, Japan; 3Department of Oncology, The First Affiliated Hospital of Gannan Medical University, Ganzhou, China

Contributions: (I) Conception and design: L Zhu, E Gao, A Li; (II) Administrative support: B Tao; (III) Provision of study materials or patients: B Tao; (IV) Collection and assembly of data: L Zhu, A Li; (V) Data analysis and interpretation: B Tao, A Li; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.

#These authors contributed equally to this work.

*These authors contributed equally to this work as co-corresponding authors.

Correspondence to: An Li, PhD. Department of Oncology, The First Affiliated Hospital of Gannan Medical University, 23 Qingnian Road, Ganzhou 341000, China. Email: lian8860562@163.com; Bo Tao, PhD. Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, 507 Zhengmin Road, Shanghai 200433, China. Email: tbo0820@163.com.

Background: Circulating tumor cells (CTCs) provide a unique resource to decipher cell molecular properties of lung cancer. However, the clinicopathologic and radiological features associated with CTCs in different lung cancer subtypes remain poorly characterized. The aim of this study was to explore the clinicopathological and radiological features of CTCs in different lung cancer subtypes.

Methods: The CTC data were obtained using the CellSearch Circulating Tumor Cell Kit. CTCs were detected in 5,128 surgical patients with lung adenocarcinoma (LUAD), 2,226 with lung squamous cell carcinoma (LUSC), 248 with small cell lung cancer (SCLC), 99 with large cell carcinoma, and 70 with metastatic carcinomas. A Pearson correlation analysis was conducted to analyze the patients’ clinical information, radiological features, and molecular characteristics, and logistic regression was used to examine the correlations between these factors and CTCs.

Results: For LUAD, the presence of tumor lobation, air bronchogram, and the epidermal growth factor receptor (EGFR) mutation were significantly associated with CTC levels. While the multivariable logistic regression analysis indicated that CD68 and P40 expression were independent factors associated with CTCs. For LUSC, tumor size, tumor spiculation, pleural indentation, air bronchogram, the expression levels of CK8/18, GPA33, and leucocyte common antigen (LCA) were significantly associated with CTC levels. The multivariable logistic regression analysis indicated that tumor size, pleural indentation, and air bronchogram were independent factors affecting CTCs. For SCLC, no factors were found to be significantly associated with CTC levels. For large cell carcinoma, tumor lobation and spiculation were significantly associated with CTC levels. For metastatic lung cancers, the presence of the positive lymphoid node was the only factor significantly associated with CTC levels.

Conclusions: We conducted a comprehensive analysis of the tumor properties, radiological features, and genomic characteristics that are significantly associated with CTCs in different lung cancer subtypes. This study helps elucidate the formation mechanism and relevant major regulation molecules of CTCs.

Keywords: Circulating tumor cells (CTCs); lung cancer; subtypes


Submitted Oct 15, 2024. Accepted for publication Nov 22, 2024. Published online Nov 28, 2024.

doi: 10.21037/tlcr-24-955


Highlight box

Key findings

• Circulating tumor cells (CTCs) are associated with different clinicopathologic and radiological features in different lung cancer subtypes.

What is known, and what is new?

• CTC levels are different in different cancers, and have been confirmed to be a useful diagnostic indicator.

• Our study revealed the specific clinicopathologic and radiological features affecting CTC levels in different lung cancer subtypes.

What is the implication, and what should change now?

• This study helps to elucidate the formation mechanism and relevant major regulation molecules of CTCs. We suggest to incorporate CTC assessments into routine clinical check-ups before surgery.


Introduction

Circulating tumor cells (CTCs) represent a distinct metastatic precursor of tumor initiation, and have potential value in monitoring malignancy progression (1). High CTC levels lead to an increased tumor burden, a low sensitivity to therapy, and a worse prognosis (2,3). Recent studies have shown that CTC-based classification could be used to reveal the tumor genomic panorama and differentiate among clinical features, thus providing an excellent tool for patient stratification in clinical setting (4,5).

Research has also shown that CTC levels can be used to identify cancers with an accuracy exceeding ordinary histopathology diagnosis (6,7); however, the use of such methods has rarely been reported in lung cancer. Recently, a bioinformatics analysis revealed that CTCs could reveal non-small cell lung cancer (NSCLC) genomic characteristics and could potentially be used in pathological classification (8). However, the associations between CTCs and patients’ clinical features were not described due to a lack of data on clinical information. The type of lung cancer is a significant factor affecting CTC levels; however, it is still unknown whether different subtypes of lung cancer, such as small cell lung cancer (SCLC) and metastatic lung cancer, have similar CTC levels and independent regulatory factors.

Further, given the multiple methods used to detect CTCs, and the mixed reference range due to the small sample sizes of previous research studies, no standard study (with matched surgical specimen confirmation) has explored in this area. To address these issues, we performed a large retrospective study to examine the baseline CTC levels of different lung cancer subtypes, including lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), large cell carcinoma, SCLC, and cancers not otherwise specified. In addition, we investigated the factors affecting CTCs, including the clinical factors, imaging aspects, and genomic molecules. We present this article in accordance with the STROBE reporting checklist (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-24-955/rc).


Methods

Patient cohort

This retrospective, single-center, cross-sectional study was performed in Shanghai, China. Between January 2020 and June 2023, 8,000 whole blood samples were collected from 8,000 patients who were hospitalized in Shanghai Pulmonary Hospital. All the patients were diagnosed with lung cancer (i.e., LUAD, LUSC, SCLC, large cell carcinoma, or other), and the diagnoses were confirmed by post-operative specimens obtained from general anesthesia surgery (wedge resection, segmentectomy, lobectomy, or other). Blood was collected from the patients after admission and before surgery. This study was independently approved by the Institutional Review Board of Shanghai Pulmonary Hospital, Shanghai, China (No. K24-539Y). The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013), International Conference on Harmonization Guidelines for Good Clinical Practice and local regulations. The patients provided informed consent to participate in the study. The patients were not compensated for participation in the study.

Blood sample and CTCs

Approximately 10 ml of whole blood was drawn from the patients before surgery and initiation of any systemic treatment and stored in an additional vial of 10 mL CellSave tube (K2E EDTA, BD-Plymouth, PL6 7BP, Plymouth, UK). The blood was temporarily placed in a refrigerator and stored at 4 ℃ awaiting plasma isolation, which was performed within 2 hours of collection, CTC assessments were performed on the CellSave tube blood, and plasma centrifugation was performed at 1,000 g for 15 minutes. After centrifugation, the residual supernatant was removed. The CTC data were analyzed using the CellSearch Circulating Tumor Cell Kit (GENO, 20163400061, Nantong, China). If the sample was not immediately used, it was divided into small parts and stored at −80 ℃ to avoid repeated freezing. We tried not to use hemolytic or hyperlipidemic blood as much as possible.

Chest computed tomography (CT) scan and imaging features

All patients enrolled in the study underwent chest CT scans within 1 month before surgery at the Radiology Department using a Siemens Somatom Definition AS scanner (Siemens Medical Systems, Erlangen, Germany) or a Philips Brilliance 40 scanner (Philips Healthcare, Cleveland, USA). After CT, thin-slice chest high-resolution CT reconstructions were conducted at contiguous 1-mm slices. Pulmonary nodules with abnormally high density in the lungs with or without suspected morphologic abnormalities were detected. Next, lesions were identified using the CT multiplanar reconstruction. The radiographic parameters, including the nodule size, location, density, lobation (with or without), spiculated margin, vacuole sign, pleural indentation, and air bronchogram, were extracted by two experienced thoracic surgeons. All the CT features were generated in both the lung window (width 1,500 HU, level 600 HU) and mediastinal window (350 HU, 50 HU).

Tumor tissue molecular testing

The resected specimens were sent to the Pathology Department to identify the tumor pathological types and molecular markers. More specifically, paraffin-embedded tissues were prepared with a thickness of approximately 3 µm. The EnVision two-step method was used for immunohistochemistry detection, and the instructions of the staining kit were strictly followed. The primary antibodies were mouse anti-rat monoclonal antibody, including TTF-1, calretinin, CD34, CDX2, CK, CK7, CK20, desmin, E-cadherin, estrogen receptor (ER), napsin A, P53, progesterone receptor (PR), Ki-67, Villin, and WT-1. Details of the antibodies are provided in Table S1. To determine the lung cancer pathological type, two independent pathologists evaluated the sample slices, and if a disagreement arose, a consensus was reached as to the tissue diagnosis.

Measurement of gene mutation

The RainDrop Plus dPCR System (California, USA) was used to detect common gene mutations, including the epidermal growth factor receptor (EGFR), anaplastic lymphoma kinase (ALK), ROS proto-oncogene 1, receptor tyrosine kinase (ROS1), Kirsten rat sarcoma viral oncogene homolog (KRAS), and v-Raf murine sarcoma viral oncogene homolog B (BRAF) mutations. First, according to the DNA sequence of the deletion region, a pair of suitable primers were synthesized on both sides of the deletion fragment for the polymerase chain reaction (PCR). Next, agarose gel electrophoresis and ethidium bromide staining were used to observe whether there were specific amplification fragments under short-wave ultraviolet light.

Statistical analysis

The patients’ clinical features, CT imaging values, and molecular data were presented in the form of the median, frequency, and percentage. For the continuous variables, the Mann-Whitney U test was used to compare the differences between the two groups. For the categorical variables, the Pearson’s Chi-squared (χ2 test) or Fisher’s test was used to compare the differences between the two groups. A Pearson correlation analysis was used to explore the relevant factors when the data were quantitative and normally distributed, and a Spearman correlation analysis was used when the data were non-normally distributed. A univariate logistic regression analysis was performed to investigate the significant factors associated with CTC, and all the significant factors in the univariate logistic regression were included in the multivariate logistic regression to identify the independent factors. The statistical analyses were performed with R software (version 4.0.3). The P values were two-sided, and a P value <0.05 was considered statistically significant.


Results

Participants’ characteristics

This study cohort comprised 8,000 patients with lung cancer confirmed by surgical pathology, and 8,000 corresponding blood samples were collected. The patients had a median age [interquartile range] of 65 [33–78] years, and 3,681 (46.01%) were male. A majority of the patients had LUAD (n=5,128, 64.10%), followed by LUSC (n=2,226, 27.83%), SCLC (n=248, 3.10%), large cell carcinoma (n=99, 1.24%), metastatic tumor (n=70, 0.88%), and a cancer not otherwise specified (n=229, 2.86%). Of the patients, 2,142 (26.78%) were active smokers, and 18.71% (n=1,497) had hypertension, 5.04% (n=403) had coronary heart disease, and 16.40% had diabetes (n=1,312).

Characteristics of CTCs in LUAD

The full clinical, radiological, and molecular characteristics of the 5,128 patients with early-stage LUAD are presented in Table 1. The average CTC level of these patients was 10.04±5.42 (range, 2.76–34.91) pg/mL, and the median was 8.85 pg/mL. Based on the Pearson correlation analysis, no significant associations were found between age, gender, underlying diseases (hypertension, coronary heart disease, and diabetes), and CTC level (P>0.053) (Table 1).

Table 1

Baseline clinical characteristics and correlation analysis of CTCs in patients with LUAD (n=5,128)

Characteristic Value Correlation coefficient P
Gender 0.101 0.21
   Male 1,902 (37.09)
   Female 3,226 (62.91)
Age (years) 59.96±9.88 −0.001 >0.99
Underlying diseases
   Hypertension 891 (17.38) 0.125 0.12
   Diabetes 928 (18.10) 0.034 0.67
   Coronary heart disease 32 (6.24) 0.074 0.36
Active smoker 1,581 (30.83) −0.057 0.48
Radiological features
   Tumor size (mm) 23.35±12.35 0.151 0.058
   Tumor lobation 2,774 (54.10) 0.680 0.043*
   Air bronchogram 291 (5.67) 0.712 0.03*
   Location
    RUL 1,965 (38.32) 0.064 0.43
    RML 417 (8.13) 0.085 0.29
    RLL 736 (14.35) 0.095 0.24
    LUL 1252 (24.41) −0.052 0.52
    LLL 758 (14.78) 0.018 0.83
   Density (HU) −186.95±258.89 0.099 0.21
   Spiculated margin 3,677 (71.70) 0.000 >0.99
   Vacuole sign 435 (8.49) −0.013 0.87
   Pleural indentation 1,613 (31.45) −0.039 0.63
   Necrosis 359 (7.00) 0.032 0.70
   Vascular wall invasion 548 (10.69) −0.092 0.25
   Pleural infiltration 1,032 (20.12) −0.011 0.89
   Perineural invasion 129 (2.52) −0.002 0.98
   Intravascular tumor thrombus 1,354 (26.40) −0.041 0.61
   Positive incision margin 8 (0.16) −0.021 0.83
   Positive lymph node 1,161 (22.64) −0.090 0.26
Genetic mutation
   EGFR-mutated 2,588 (50.47) 0.634 0.047*
   ALK-mutated 297 (5.79) 0.150 0.06
   ROS1-mutated 748 (14.59) 0.155 0.052
   KRAS-mutated 672 (13.10) 0.148 0.06
   BRAF-mutated 309 (6.03) 0.132 0.10

Data are presented as n (%), or mean ± standard deviation. *, P<0.05. CTC, circulating tumor cell; LUAD, lung adenocarcinoma; RUL, right upper lobe; RML, right middle lobe; RLL, right lower lobe; LUL, left upper lobe; LLL, left lower lobe; HU, Hounsfield unit; EGFR, epidermal growth factor receptor; ALK, anaplastic lymphoma kinase; ROS1, ROS proto-oncogene 1, receptor tyrosine kinase; KRAS, Kirsten rat sarcoma viral oncogene homolog; BRAF, v-Raf murine sarcoma viral oncogene homolog B.

To assess the potential effects of the radiological features on CTCs, we compared the tumor density, size, distance from pleura, lobation, spiculation, vascular convergence, pleural indentation, and air bronchogram, etc. The analysis of these imaging characteristics revealed that the presence of tumor lobation (r=0.680, P=0.04) and air bronchogram (r=0.712, P=0.03) were positively associated with CTCs.

To further explore the factors affecting CTCs, we investigated the clinicopathologic and genomic characteristics using the pathological specimens resected from surgery. The results showed that the expression of CK (r=0.701, P=0.03), CD68 (r=0.749, P=0.03), and P40 (r=0.821, P=0.02) were significantly associated with CTCs. A previous study reported that the CTC levels were affected by EGFR mutation status in NSCLC (9). Therefore, we also calculated the coefficients between the CTCs and EGFR, ALK, ROS1, KRAS, and BRAF mutations. However, the mutation data results were not statistically significant, except in relation to the EGFR mutation (r=0.634, P=0.047) (Table 2). The patients were divided into the CTC-high (CTC-H) and CTC-low (CTC-L) groups based on the median. We conducted a logistic regression to explore the clinical, pathologic, and genomic characteristics associated with CTCs. The multivariable logistic regression analysis indicated that CD68 [odds ratio (OR) =1.56, 95% confidence interval (CI): 0.78–3.77, P=0.045] and P40 (OR =3.87, 95% CI: 1.53–4.68, P=0.009) expression were independent factors affecting CTCs (Table 3).

Table 2

Correlations between CTC and molecular expression in LUAD

Molecular expression Negative, N (%) Positive, N (%) Unknown N (%) Correlation coefficient P
TTF-1 (10%) 513 (10.00) 4,615 (90.00) 0.000 −0.050 0.54
Calretinin (5%) 123 (2.40) 70 (1.37) 4,935 (96.24) −0.042 0.60
CD34 (20%) 33 (0.64) 140 (2.73) 4,955 (96.63) −0.062 0.44
CD56 (5%) 322 (6.28) 642 (12.52) 4,164 (81.20) 0.083 0.30
CD68 (30%) 95 (1.85) 129 (2.52) 4,904 (95.63) 0.749 0.03*
CDX2 (50%) 152 (2.96) 99 (1.93) 4,877 (95.11) −0.044 0.58
C-MET (25%) 312 (6.08) 1,677 (32.70) 3,139 (61.21) −0.092 0.25
CgA (10%) 31 (0.60) 98 (1.91) 4,999 (97.48) 0.421 0.19
CK (10%) 269 (5.25) 1,764 (34.40) 3,095 (60.35) 0.701 0.03*
CK20 (10%) 34 (0.66) 260 (5.07) 4,837 (94.33) 0.044 0.59
CK7 (10%) 32 (0.62) 291 (5.67) 4,805 (93.70) 0.101 0.21
CK8/18 (10%) 112 (2.18) 4,004 (78.08) 1,012 (19.73) 0.013 0.87
Desmin (1%) 189 (3.69) 116 (2.26) 4,823 (94.05) 0.102 0.20
E-cadherin (25%) 418 (8.15) 2,769 (54.00) 1,941 (37.85) 0.014 0.86
ER (25%) 366 (71.37) 111 (2.16) 4,651 (90.70) 0.454 0.11
GATA3 (50%) 225 (4.39) 33 (6.44) 4,870 (94.97) 0.119 0.14
GATA6 (10%) 37 (0.72) 1,931 (37.66) 3,160 (61.62) 0.012 0.88
GPA33 (1%) 332 (6.47) 97 (1.89) 4,699 (91.63) −0.031 0.70
HER2 (50%) 910 (17.75) 2,161 (42.14) 2,057 (40.11) 0.013 0.87
HNF4α (1%) 349 (6.81) 484 (9.44) 4,295 (83.76) −0.008 0.92
Ki-67 (25%) 42 (0.82) 839 (16.36) 4,247 (82.82) 0.106 0.19
LCA (10%) 181 (3.53) 52 (1.01) 4,895 (95.46) −0.081 0.31
MTAP (50%) 37 (0.72) 78 (1.52) 5,013 (97.76) −0.087 0.28
MUC4 (5%) 506 (9.87) 10 (0.20) 4,612 (89.94) −0.110 0.17
Napsin A (25%) 1,129 (22.02) 3,967 (77.36) 32 (0.62) 0.032 0.69
P40 (10%) 2,104 (41.03) 276 (5.38) 2,748 (53.59) 0.821 0.02*
P53 (50%) 843 (16.44) 2,243 (43.74) 2,042 (39.82) −0.091 0.25
Pou2F3 (1%) 289 (5.64) 194 (3.78) 4,645 (90.58) −0.005 0.95
PD-L1 (1%) 801 (15.62) 4,020 (78.39) 307 (5.99) −0.037 0.65
PR (5%) 980 (19.11) 76 (1.48) 4,072 (79.41) 0.080 0.32
SALL4 (25%) 632 (12.32) 3,456 (67.39) 1,040 (20.28) −0.149 0.06
SMARCA4 (25%) 2,159 (42.10) 2,705 (52.75) 264 (5.15) 0.055 0.49
Villin (10%) 1,937 (37.77) 2,923 (57.00) 268 (5.23) −0.028 0.73
WT-1 (1%) 204 (3.98) 9 (0.18) 4,915 (95.85) −0.076 0.35
ZEB1 (10%) 1,512 (29.49) 2,205 (43.00) 1,411 (27.52) 0.103 0.20
β-catenin (50%) 1,881 (36.68) 1,589 (29.80) 1,658 (32.33) 0.485 0.67

For molecular expression, the percentage in parentheses represents the threshold for positive and negative expressions. *, P<0.05. CTC, circulating tumor cell; LUAD, lung adenocarcinoma; TTF-1, thyroid transcription factor-1; CK, cytokeratin; C-MET, cellular-mesenchymal to epithelial transition factor; LCA, leucocyte common antigen; MTAP, methylthioadenosine phosphorylase; ER, estrogen receptor; PD-L1, programmed cell death ligand 1; PR, progesterone receptor; SALL4, spalt-like transcription factor 4; SMARCA4, SWI/SNF-related, matrix-associated, actin-dependent regulator of chromatin; WT-1, Wilms tumor 1; ZEB1, zinc finger E-box binding homeobox 1.

Table 3

The multivariable logistic analysis of factors associated with CTCs in LUAD and LUSC

Variables CTCs
OR 95% CI P value
LUAD
   CD68 1.56 0.78–3.77 0.045
   P40 3.87 1.53–4.68 0.009
LUSC
   Tumor size 2.29 0.13–5.46 0.01
   Pleural indentation 1.66 0.39–4.33 0.02
   Air bronchogram 1.37 0.79–2.87 0.04

CTC, circulating tumor cell; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; OR, odds ratio; CI, confidence interval.

Characteristics of CTCs in LUSC

The full clinical, radiological, and molecular characteristics of the 2,226 patients with LUSC are presented in Table 3. The average CTC level of these patients was 13.12±6.20 (5.90–32.81) pg/mL, and the median was 11.59 pg/mL. No significant associations were found between age, gender, underlying diseases (hypertension, coronary heart disease, and diabetes), and CTC level.

In relation to the radiological features, tumor size (r=0.849, P=0.02), tumor spiculation (r=0.670, P=0.03), pleural indentation (r=0.562, P=0.046), and air bronchogram (r=0.713, P=0.02) were found to be significantly associated with CTC levels (Table 4).

Table 4

Baseline clinical characteristics and correlation analysis of CTCs in patients with LUSC (n=2,226)

Characteristic Value Correlation coefficient P
Gender 0.033 0.86
   Male 1,791 (80.46)
   Female 435 (19.54)
Age (years) 64.26±6.01 0.228 0.22
Underlying diseases
   Hypertension 352 (15.81) −0.265 0.15
   Diabetes 360 (16.17) −0.122 0.52
   Coronary heart disease 105 (4.72) 0.601 0.54
Active smoker 581 (26.10) −0.230 0.21
Radiological features
   Tumor size (mm) 30.01±19.11 0.849 0.02*
   Tumor lobation 1,461 (65.63) 0.329 0.07
   Air bronchogram 487 (21.88) 0.713 0.02*
   Location
    RUL 903 (40.57) 0.129 0.49
    RML 156 (7.01) 0.142 0.45
    RLL 647 (29.07) 0.061 0.75
    LUL 239 (10.74) 0.181 0.33
    LLL 281 (12.62) 0.053 0.78
   Density (HU) 49.35±16.42 0.200 0.70
   Spiculated margin 1,183 (53.14) 0.670 0.03*
   Vacuole sign 61 (2.74) 0.196 0.29
   Pleural indentation 417 (18.73) 0.562 0.046*
   Necrosis 833 (37.42) 0.024 0.90
   Vascular wall invasion 69 (3.10) −0.115 0.54
   Pleural infiltration 44 (1.98) 0.080 0.67
   Perineural invasion 28 (1.26) 0.092 0.62
   Intravascular tumor thrombus 37 (1.66) 0.148 0.39
   Positive incision margin 4 (0.18) −0.096 0.61
   Positive lymph node 766 (34.41) 0.106 0.57
Genetic mutation
   EGFR-mutated 342 (15.36) 0.298 0.10
   ALK-mutated 93 (4.18) 0.105 0.57
   ROS1-mutated 35 (1.57) 0.196 0.48
   KRAS-mutated 192 (8.63) 0.069 0.71
   BRAF-mutated 73 (3.28) 0.120 0.29

Data are presented as n (%), or mean ± standard deviation. *, P<0.05. CTC, circulating tumor cell; LUAD, lung adenocarcinoma; RUL, right upper lobe; RML, right middle lobe; RLL, right lower lobe; LUL, left upper lobe; LLL, left lower lobe; HU, Hounsfield unit; EGFR, epidermal growth factor receptor; ALK, anaplastic lymphoma kinase; ROS1, ROS proto-oncogene 1, receptor tyrosine kinase; KRAS, Kirsten rat sarcoma viral oncogene homolog; BRAF, v-Raf murine sarcoma viral oncogene homolog B.

In relation to the genomic characteristics, CK8/18 (r=−0.691, P=0.03), GATA6 (r=0.655, P=0.03), and leucocyte common antigen (LCA) (r=−0.046, P=0.81) were found to be significantly associated with CTC levels. No significant associations were found between the EGFR, ALK, ROS1, KRAS, and BRAF mutation and CTCs (Table 5).

Table 5

Correlations between CTC and molecular expression in LUSC

Molecular expression Negative, N (%) Positive, N (%) Unknown, N (%) Clinical information P
TTF-1 (10%) 1,127 (50.63) 646 (29.02) 453 (20.35) −0.200 0.28
Calretinin (5%) 83 (3.73) 36 (1.62) 2,107 (94.65) −0.004 0.98
CD34 (20%) 91 (4.09) 39 (1.75) 2,096 (94.16) −0.115 0.55
CD56 (5%) 98 (4.40) 43 (1.93) 2,085 (93.67) 0.145 0.44
CD68 (30%) 137 (6.15) 209 (9.39) 1,880 (84.46) −0.156 0.40
CDX2 (50%) 61 (2.74) 26 (1.17) 2,139 (96.09) −0.189 0.31
c-MET (25%) 37 (1.66) 45 (2.02) 2,144 (96.32) 0.179 0.34
CgA (10%) 53 (2.38) 40 (1.80) 2,133 (95.82) −0.042 0.83
CK (10%) 767 (34.46) 1,044 (46.90) 415 (18.64) 0.148 0.43
CK20 (10%) 198 (8.89) 210 (9.43) 1,818 (81.67) 0.039 0.84
CK7 (10%) 29 (1.30) 288 (12.94) 1,909 (85.76) 0.233 0.21
CK8/18 (10%) 10 (0.45) 213 (9.57) 2,003 (89.98) −0.691 0.03
Desmin (1%) 176 (7.91) 17 (0.76) 2,033 (91.33) −0.060 0.75
E-cadherin (25%) 117 (5.26) 63 (2.83) 2,046 (91.91) 0.099 0.60
ER (25%) 112 (5.03) 48 (2.16) 2,066 (92.81) 0.304 0.10
GATA3 (50%) 71 (3.19) 834 (37.47) 1,321 (59.34) 0.026 0.89
GATA6 (10%) 344 (15.45) 402 (18.06) 1,480 (66.49) 0.655 0.03*
GPA33 (1%) 79 (3.55) 92 (4.13) 2,056 (92.36) 0.170 0.36
HER2 (50%) 101 (4.54) 279 (12.53) 1,846 (82.93) −0.006 0.98
HNF4α (1%) 49 (2.20) 178 (8.00) 1,999 (89.80) −0.002 0.99
Ki-67 (25%) 146 (6.56) 514 (23.09) 1,566 (70.35) 0.214 0.25
LCA (10%) 57 (2.56) 74 (3.32) 2,095 (94.12) −0.046 0.81
MTAP (50%) 38 (1.71) 20 (0.90) 2,168 (97.39) 0.559 0.047*
MUC4 (5%) 18 (0.81) 55 (2.47) 2,153 (96.72) 0.169 0.37
Napsin A (25%) 376 (16.89) 967 (43.44) 883 (39.67) 0.154 0.24
P40 (10%) 140 (6.29) 1,984 (89.13) 102 (4.58) 0.066 0.73
P53 (50%) 702 (31.54) 1,330 (59.75) 194 (8.72) 0.270 0.14
Pou2F3 (1%) 154 (6.92) 53 (2.38) 2,019 (90.70) 0.055 0.77
PD-L1 (1%) 543 (24.39) 1,263 (56.74) 420 (18.87) −0.012 0.95
PR (5%) 21 (0.94) 47 (2.11) 2,158 (96.95) −0.101 0.59
SALL4 (25%) 663 (29.78) 1,116 (50.13) 447 (20.08) 0.177 0.34
SMARCA4 (25%) 418 (18.78) 1,456 (65.41) 352 (15.81) 0.178 0.35
Villin (10%) 164 (7.37) 23 (1.03) 2,039 (91.60) −0.089 0.63
WT-1 (1%) 70 (3.14) 89 (4.00) 2,067 (92.86) 0.080 0.67
ZEB1 (10%) 528 (23.72) 451 (20.26) 1,247 (56.02) −0.093 0.62
β-catenin (50%) 111 (4.99) 742 (33.33) 1,373 (61.68) 0.215 0.25

For molecular expression, the percentage in parentheses represents the threshold for positive and negative expressions. *, P<0.05. CTC, circulating tumor cell; LUSC, lung squamous cell carcinoma; TTF-1, thyroid transcription factor-1; CK, cytokeratin; C-MET, cellular-mesenchymal to epithelial transition factor; LCA, leucocyte common antigen; MTAP, methylthioadenosine phosphorylase; ER, estrogen receptor; PD-L1, programmed cell death ligand 1; PR, progesterone receptor; SALL4, spalt-like transcription factor 4; SMARCA4, SWI/SNF-related, matrix-associated, actin-dependent regulator of chromatin; WT-1, Wilms tumor 1; ZEB1, zinc finger E-box binding homeobox 1.

Similarly, the patients were also divided into CTC-H and CTC-L groups based on the median. The multivariable logistic regression analysis revealed that tumor size (OR =2.29, 95% CI: 0.13–5.46, P=0.01), pleural indentation (OR =1.66, 95% CI: 0.39–4.33, P=0.02), and air bronchogram (OR =1.37, 95% CI: 0.79–2.87, P=0.04) were independent factors affecting CTCs in LUSC (Table 3).

Characteristics of CTCs in SCLC

In terms of its radiological features, compared to NSCLC, SCLC has few distinguishing differences, especially in patients without any clinical symptoms. All the SCLC patients presented with a single nodule, ranging from 9.20–56 millimeters, with no distant metastasis. The full clinical, radiological, and molecular characteristics of the 248 patients with SCLC are presented in Table 6. The average CTC level of these patients was 10.31±2.02 (range, 3.26–22.26) pg/mL, and the median was 10.67 pg/mL. The expression levels of TTF1 (r=−0.289, P=0.64), CgA (r=−0.350, P=0.58), CK7 (r=−0.707, P=0.18), CD56 (r=−0.354, P=0.56), CDX2 (r=−0.272, P=0.50), GATA3 (r=−0.667, P=0.22), and Pou2F3 (r=−0.297, P=0.43), and the presence of spiculation (r=−0.439, P=0.18) were negatively correlated with CTCs, but none reached statistical significance. The expression levels of SMARCA4 (r=−0.166, P=0.36), Ki-67 (r=−0.095, P=0.98), and GATA6 (r=−0.171, P=0.44), and tumor size (r=−0.331 P=0.08) were positively correlated with CTCs, but none reached statistical significance (Table 7).

Table 6

Baseline clinical characteristics and correlation analysis of CTCs in patients with small cell carcinoma (n=248)

Characteristic Value Correlation coefficient P
Gender −0.047 0.80
   Male 176 (70.97)
   Female 72 (29.03)
Age (years) 58.20±7.79 0.336 0.58
Underlying diseases
   Hypertension 36 (14.52) −0.068 0.71
   Diabetes 29 (11.69) 0.143 0.44
   Coronary heart disease 9 (3.63) 0.074 0.69
Active smoker 45 (18.15) 0.527 0.23
Radiological features
   Tumor size (mm) 28.20±7.79 −0.331 0.08
   Tumor lobation 185 (74.60) 0.058 0.75
   Air bronchogram 23 (9.27) −0.232 0.20
   Location
    RUL 38 (15.32) 0.338 0.58
    RML 20 (8.06) −0.022 0.91
    RLL 24 (9.68) 0.271 0.16
    LUL 76 (30.65) 0.183 0.40
    LLL 90 (36.29) −0.057 0.77
   Density (HU) 33.50±24.45 0.095 0.61
   Spiculated margin 32 (12.90) −0.439 0.18
   Vacuole sign 16 (6.45) 0.062 0.75
   Pleural indentation 62 (25.00) 0.018 0.76
   Necrosis 8 (3.23) 0.074 0.69
   Vascular wall invasion 70 (28.23) −0.169 0.36
   Pleural infiltration 61 (24.60) −0.111 0.55
   Perineural invasion 63 (25.40) −0.222 0.22
   Intravascular tumor thrombus 96 (38.71) 0.116 0.53
   Positive incision margin 0.000 0.303 0.09
   Positive lymph node 167 (67.34) −0.118 0.52
Genetic mutation 0.258 0.58
   EGFR-mutated 23 (9.27) 0.216 0.26
   ALK-mutated 2 (0.81) 0.322 0.07
   ROS1-mutated 3 (1.21) −0.155 0.40
   KRAS-mutated 7 (2.82) 0.014 0.94
   BRAF-mutated 0.000 0.275 0.13

Data are presented as n (%), or mean ± standard deviation. CTC, circulating tumor cell; LUAD, lung adenocarcinoma; RUL, right upper lobe; RML, right middle lobe; RLL, right lower lobe; LUL, left upper lobe; LLL, left lower lobe; HU, Hounsfield unit; EGFR, epidermal growth factor receptor; ALK, anaplastic lymphoma kinase; ROS1, ROS proto-oncogene 1, receptor tyrosine kinase; KRAS, Kirsten rat sarcoma viral oncogene homolog; BRAF, v-Raf murine sarcoma viral oncogene homolog B.

Table 7

Correlations between CTC and molecular expression in lung small cell carcinoma

Molecular expression Negative, N (%) Positive, N (%) Unknown, N (%) Correlation coefficient P
TTF-1 (10%) 21 (8.47) 153 (61.69) 74 (29.84) −0.289 0.64
Calretinin (5%) 10 (4.03) 0.000 238 (95.97) 0.116 0.59
CD34 (20%) 71 (28.63) 30 (12.10) 147 (59.27) 0.289 0.11
CD56 (5%) 33 (13.31) 202 (81.45) 13 (5.24) −0.354 0.56
CD68 (30%) 31 (12.50) 4 (1.61) 213 (85.89) 0.022 0.89
CDX2 (50%) 5 (2.02) 0.000 243 (97.98) −0.272 0.50
c-MET (25%) 37 (14.92) 6 (2.42) 205 (82.66) −0.031 0.87
CgA (10%) 47 (18.95) 146 (58.87) 55 (22.18) −0.350 0.58
CK (10%) 66 (26.61) 93 (37.50) 89 (35.89) −0.164 0.34
CK20 (10%) 43 (17.34) 11 (4.44) 194 (28.23) −0.120 0.52
CK7 (10%) 49 (19.76) 142 (57.26) 57 (22.98) −0.707 0.18
CK8/18 (10%) 26 (10.48) 0.000 222 (89.52) 0.230 0.71
Desmin (1%) 7 (2.82) 0.000 241 (97.18) −0.108 0.56
E-cadherin (25%) 104 (41.94) 27 (10.89) 117 (47.185) 0.464 0.50
ER (25%) 14 (5.65) 0.000 234 (94.35) −0.102 0.58
GATA3 (50%) 8 (3.23) 0.000 240 (96.77) −0.667 0.22
GATA6 (10%) 174 (70.16) 50 (20.16) 24 (9.68) −0.171 0.44
GPA33 (1%) 22 (8.87) 0.000 226 (91.13) −0.036 0.85
HER2 (50%) 35 (14.11) 15 (6.05) 198 (79.84) 0.242 0.19
HNF4α (1%) 12 (4.84) 0.000 236 (95.16) −0.073 0.70
Ki-67 (25%) 28 (11.29) 170 (68.55) 50 (20.16) −0.095 0.98
LCA (10%) 23 (9.27) 0.000 225 (90.73) 0.080 0.67
MTAP (50%) 18 (7.26) 0.000 230 (92.74) −0.373 0.54
MUC4 (5%) 16 (6.45) 0.000 232 (93.55) 0.419 0.48
Napsin A (25%) 194 (78.23) 3 (1.21) 51 (20.56) −0.152 0.91
P40 (10%) 39 (15.73) 10 (4.03) 199 (80.24) 0.026 0.81
P53 (50%) 56 (22.58) 159 (64.11) 33 (13.31) −0.447 0.23
Pou2F3 (1%) 30 (12.90) 53 (21.37) 165 (66.53) −0.297 0.43
PD-L1 (1%) 136 (54.84) 58 (23.39) 54 (21.77) −0.010 0.96
PR (5%) 17 (6.85) 2 (0.81) 229 (92.34) 0.011 0.96
SALL4 (25%) 133 (53.63) 25 (10.08) 90 (36.29) 0.049 0.79
SMARCA4 (25%) 82 (33.06) 34 (13.71) 132 (53.23) −0.166 0.36
Villin (10%) 32 (12.90) 0.000 216 (87.10) 0.081 0.66
WT-1 (1%) 41 (16.53) 0.000 207 (83.47) −0.079 0.71
ZEB1 (10%) 8 (3.22) 0.000 240 (96.77) −0.167 0.43
β-catenin (50%) 48 (19.35) 42 (16.94) 158 (63.71) −0.046 0.80

For molecular expression, the percentage in parentheses represents the threshold for positive and negative expressions. CTC, circulating tumor cell; TTF-1, thyroid transcription factor-1; CK, cytokeratin; C-MET, cellular-mesenchymal to epithelial transition factor; LCA, leucocyte common antigen; MTAP, methylthioadenosine phosphorylase; ER, estrogen receptor; PD-L1, programmed cell death ligand 1; PR, progesterone receptor; SALL4, spalt-like transcription factor 4; SMARCA4, SWI/SNF-related, matrix-associated, actin-dependent regulator of chromatin; WT-1, Wilms tumor 1; ZEB1, zinc finger E-box binding homeobox 1.

Characteristics of CTCs in large cell carcinoma

The full clinical, radiological, and molecular characteristics of the 99 patients with large cell carcinoma are presented in Table 8. The average CTC level of these patients was 11.55±2.43 (range, 3.19–21.98) pg/mL, and the median was 11.76 pg/mL. As Table 9 shows, the expression levels of SMARCA4 (r=0.811, P=0.01), CgA (r=0.719, P=0.04), CD56 (r=0.730, P=0.03), and GATA6 (r=0.623, P=0.046) were significantly associated with CTCs. The results of the radiological features analysis showed that nodule lobation (r=0.641, P=0.04) and spiculation (r=0.736, P=0.03) were significantly associated with CTC levels. The univariate and multivariable logistic regression analyses found that no factors were significantly associated with CTCs (Table 9).

Table 8

Baseline clinical characteristics and correlation analysis of CTCs in patients with large cell carcinoma (n=99)

Characteristic Value Correlation coefficient P
Gender −0.235 0.70
   Male 56 (56.57)
   Female 43 (43.43)
Age (years) 65.67±9.01 0.368 0.54
Underlying diseases
   Hypertension 13 (13.13) 0.183 0.77
   Diabetes 14 (14.14) −0.135 0.83
   Coronary heart disease 3 (3.03) 0.611 0.27
Active smoker 19 (19.19) 0.374 0.54
Radiological features
   Tumor size (mm) 18.63±10.44 −0.066 0.82
   Tumor lobation 61 (61.62) 0.641 0.04*
   Air bronchogram 20 (20.20) −0.239 0.39
   Location
    RUL 32 (32.32) −0.297 0.30
    RML 8 (8.08) 0.293 0.27
    RLL 23 (23.23) 0.048 0.86
    LUL 19 (19.19) 0.378 0.15
    LLL 17 (17.17) −0.228 0.40
   Density (HU) −23.25±150.17 0.476 0.83
   Spiculated margin 54 (54.55) 0.736 0.03*
   Vacuole sign 6 (6.06) −0.271 0.34
   Pleural indentation 62 (62.63) −0.500 0.67
   Necrosis 19 (19.19) −0.130 0.62
   Vascular wall invasion 4 (4.04) 0.211 0.42
   Pleural infiltration 21 (21.21) −0.292 0.26
   Perineural invasion 5 (5.05) 0.120 0.64
   Intravascular tumor thrombus 32 (32.32) −0.467 0.059
   Positive incision margin 1 (1.01) 0.694 0.80
   Positive lymph node 28 (28.28) 0.138 0.67
Genetic mutation
   EGFR-mutated 5 (5.05) −0.083 0.79
   ALK-mutated 5 (5.05) 0.365 0.22
   ROS1-mutated 0.000 −0.123 0.69
   KRAS-mutated 40 (40.40) 0.076 0.61
   BRAF-mutated 0.000 0.234 0.20

Data are presented as n (%), or mean ± standard deviation. *, P<0.05. CTC, circulating tumor cell; LUAD, lung adenocarcinoma; RUL, right upper lobe; RML, right middle lobe; RLL, right lower lobe; LUL, left upper lobe; LLL, left lower lobe; HU, Hounsfield unit; EGFR, epidermal growth factor receptor; ALK, anaplastic lymphoma kinase; ROS1, ROS proto-oncogene 1, receptor tyrosine kinase; KRAS, Kirsten rat sarcoma viral oncogene homolog; BRAF, v-Raf murine sarcoma viral oncogene homolog B.

Table 9

Correlations between CTC and molecular expression in lung large cell carcinoma

Molecular expression Negative, N (%) Positive, N (%) Unknown, N (%) Correlation coefficient P
TTF-1 (10%) 33 (33.33) 42 (42.42) 24 (24.24) −0.357 0.16
Calretinin (5%) 5 (5.05) 2 (2.02) 92 (92.93) 0.553 0.70
CD34 (20%) 10 (10.10) 3 (3.03) 86 (86.87) 0.250 0.12
CD56 (5%) 34 (34.34) 44 (44.44) 21 (21.21) 0.730 0.03*
CD68 (30%) 15 (15.15) 9 (9.09) 75 (75.76) −0.061 0.82
CDX2 (50%) 7 (7.07) 2 (2.02) 90 (90.91) 0.169 0.52
C-MET (25%) 16 (16.16) 6 (6.06) 77 (77.78) 0.034 0.56
CgA (10%) 20 (20.20) 41 (41.41) 38 (38.38) 0.719 0.04*
CK (10%) 6 (6.06) 47 (47.47) 46 (46.46) −0.140 0.59
CK20 (10%) 11 (11.11) 3 (3.03) 85 (85.86) −0.408 0.10
CK7 (10%) 26 (26.26) 4 (4.04) 69 (69.70) −0.059 0.82
CK8/18 (10%) 27 (27.27) 50 (50.51) 22 (22.22) 0.078 0.77
Desmin (1%) 13 (13.13) 7 (7.07) 79 (79.80) 0.388 0.52
E-cadherin (25%) 3 (3.03) 1 (1.01) 95 (95.96) 0.162 0.23
ER (25%) 12 (12.12) 0.000 87 (87.88) 0.316 0.20
GATA3 (50%) 14 (14.14) 0.000 85 (85.86) −0.343 0.18
GATA6 (10%) 51 (55.56) 23 (23.23) 25 (25.25) 0.623 0.046*
GPA33 (1%) 16 (16.16) 0.000 83 (83.84) 0.236 0.51
HER2 (50%) 12 (12.12) 6 (6.06) 80 (80.81) 0.178 0.86
HNF4α (1%) 8 (8.08) 0.000 91 (91.92) 0.557 0.22
Ki-67 (25%) 15 (15.15) 84 (84.85) 0.000 0.119 0.67
LCA (10%) 6 (6.06) 0.000 93 (93.94) −0.086 0.74
MTAP (50%) 4 (4.04) 0.000 95 (95.96) 0.164 0.33
MUC4 (5%) 2 (2.02) 0.000 97 (97.98) 0.569 0.78
Napsin A (25%) 47 (47.47) 52 (52.53) 0.000 0.307 0.11
P40 (10%) 18 (18.18) 0.000 81 (81.82) 0.704 0.07
P53 (50%) 10 (10.10) 66 (66.67) 23 (23.23) 0.278 0.59
Pou2F3 (1%) 5 (5.05) 0.000 94 (94.95) −0.030 0.91
PD-L1 (1%) 28 (28.28) 37 (37.37) 34 (34.34) −0.064 0.81
PR (5%) 11 (11.11) 1 (1.01) 87 (87.88) 0.318 0.23
SALL4 (25%) 51 (51.52) 13 (13.13) 35 (35.35) 0.259 0.37
SMARCA4 (25%) 45 (45.45) 54 (54.55) 0.000 0.811 0.01*
Villin (10%) 9 (9.09) 0.000 90 (90.91) 0.082 0.16
WT-1 (1%) 17 (17.17) 0.000 82 (82.83) −0.130 0.61
ZEB1 (10%) 5 (5.05) 0.000 94 (94.95) 0.454 0.059
β-catenin (50%) 36 (36.36) 0.000 63 (63.64) 0.426 0.09

For molecular expression, the percentage in parentheses represents the threshold for positive and negative expressions. *, P<0.05. CTC, circulating tumor cell; TTF-1, thyroid transcription factor-1; CK, cytokeratin; C-MET, cellular-mesenchymal to epithelial transition factor; LCA, leucocyte common antigen; MTAP, methylthioadenosine phosphorylase; ER, estrogen receptor; PD-L1, programmed cell death ligand 1; PR, progesterone receptor; SALL4, spalt-like transcription factor 4; SMARCA4, SWI/SNF-related, matrix-associated, actin-dependent regulator of chromatin; WT-1, Wilms tumor 1; ZEB1, zinc finger E-box binding homeobox 1.

Characteristics of CTCs in metastatic lung cancer

Given that metastatic lung cancers present molecular and cellular features distinct from different original malignancies, we systematically explored the clinical and radiological features, but not the molecular characteristics. Most lung metastases (n=48, 68.57%) manifested as multiple nodules on CT imaging, and the most common primary cancer were gastrointestinal cancer (n=29, 41.43%), lung cancer (n=17, 24.29%), breast cancer (n=5, 7.14%), nasopharyngeal carcinoma (n=4, 5.71%), and other (n=15, 21.43%). The average CTC level of these patients was 12.35±3.45 (range, 4.53–25.16) pg/mL, and the median was 10.40 pg/mL (Table 10). The correlation analysis showed that positive lymphoid node status was the only factor significantly associated with CTC level (r=0.829, P=0.02) (Table 11).

Table 10

Baseline clinical characteristics and correlation analysis of CTCs in patients with metastatic cancer (n=70)

Characteristic Value Correlation coefficient P
Gender 0.782 0.07
   Male 21 (30.00)
   Female 49 (70.00)
Age (year) 48.83±12.37 0.606 0.20
Underlying diseases
   Hypertension 7 (10.00) −0.027 0.78
   Diabetes 12 (17.14) −0.031 0.76
   Coronary heart disease 4 (5.71) 0.136 0.16
Active smoker 22 (31.43) −0.019 0.85
Radiological features
   Tumor size (mm) 26.87±17.55 0.187 0.06
   Tumor lobation 2 (2.86) 0.024 0.81
   Air bronchogram 3 (4.29) 0.126 0.12
   Location
    RUL 28 (40.00) 0.106 0.28
    RML 4 (5.71) 0.145 0.65
    RLL 13 (18.57) 0.014 0.89
    LUL 10 (14.29) 0.142 0.15
    LLL 15 (21.43) 0.124 0.20
   Density (HU) −234.25±350.29 −0.056 0.57
   Spiculated margin 23 (32.86) −0.076 0.43
   Vacuole sign 6 (8.57) −0.088 0.37
   Pleural indentation 12 (17.14) 0.041 0.68
   Necrosis 8 (11.43) −0.001 >0.99
   Vascular wall invasion 5 (7.14) −0.134 0.17
   Pleural infiltration 4 (5.71) −0.121 0.22
   Perineural invasion 3 (4.29) −0.117 0.23
   Intravascular tumor thrombus 1 (1.43) −0.018 0.96
   Positive incision margin 0.000
   Positive lymph node 9 (12.86) 0.829 0.02
Genetic mutation
   EGFR-mutated 8 (11.43) −0.018 0.87
   ALK-mutated 5 (7.14) −0.092 0.35
   ROS1-mutated 0.000 −0.032 0.74
   KRAS-mutated 3 (4.29) −0.068 0.49
   BRAF-mutated 6 (8.57) −0.094 0.33

Data are presented as n (%), or mean ± standard deviation. CTC, circulating tumor cell; LUAD, lung adenocarcinoma; RUL, right upper lobe; RML, right middle lobe; RLL, right lower lobe; LUL, left upper lobe; LLL, left lower lobe; HU, Hounsfield unit; EGFR, epidermal growth factor receptor; ALK, anaplastic lymphoma kinase; ROS1, ROS proto-oncogene 1, receptor tyrosine kinase; KRAS, Kirsten rat sarcoma viral oncogene homolog; BRAF, v-Raf murine sarcoma viral oncogene homolog B.

Table 11

Correlations between CTC and molecular expression in lung metastatic cancer

Molecular expression Negative, N (%) Positive, N (%) Unknown, N (%) Correlation coefficient P
TTF-1 (10%) 50 (71.43) 14 (20.00) 6 (8.57) −0.447 0.37
Calretinin (5%) 4 (5.71) 0.000 66 (94.29) 0.049 0.62
CD34 (20%) 23 (32.86) 19 (27.14) 28 (40.00) 0.186 0.056
CD56 (5%) 8 (11.43) 0.000 62 (88.57) 0.083 0.40
CD68 (30%) 20 (28.57) 31 (44.29) 19 (27.14) 0.010 0.92
CDX2 (50%) 14 (20.00) 47 (67.14) 9 (12.86) 0.137 0.16
C-MET (25%) 16 (22.86) 6 (8.57) 48 (68.57) −0.161 0.10
CgA (10%) 10 (14.29) 0.000 60 (85.71) −0.060 0.54
CK (10%) 5 (7.14) 0.000 65 (92.86) 0.127 0.19
CK20 (10%) 11 (15.71) 1 (1.43) 58 (82.86) 0.088 0.37
CK7 (10%) 19 (27.14) 39 (55.71) 12 (17.14) −0.015 0.88
CK8/18 (10%) 8 (11.43) 5 (7.14) 57 (81.43) 0.123 0.21
Desmin (1%) 15 (21.43) 0.000 55 (78.57) −0.051 0.60
E-cadherin (25%) 35 (50.00) 18 (25.71) 17 (24.29) −0.103 0.29
ER (25%) 2 (2.86) 5 (7.14) 63 (90.00) 0.078 0.43
GATA3 (50%) 21 (30.00) 12 (17.14) 37 (52.86) 0.039 0.69
GATA6 (10%) 36 (51.43) 7 (10.00) 27 (38.57) −0.084 0.39
GPA33 (1%) 3 (4.29) 0.000 67 (95.71) −0.022 0.82
HER2 (50%) 0.000 5 (7.14) 65 (92.86) 0.057 0.56
HNF4α (1%) 9 (12.86) 0.000 61 (87.14) −0.018 0.85
Ki-67 (25%) 20 (28.57) 42 (60.00) 8 (11.43) −0.062 0.53
LCA (10%) 5 (7.14) 1 (1.43) 64 (91.43) 0.017 0.86
MTAP (50%) 7 (10.00) 0.000 63 (90.00) −0.134 0.17
MUC4 (5%) 20 (28.57) 13 (18.57) 37 (52.86) 0.118 0.23
Napsin A (25%) 32 (45.71) 6 (8.57) 32 (45.71) 0.238 0.16
P40 (10%) 12 (17.14) 0.000 58 (82.86) −0.001 0.99
P53 (50%) 11 (15.71) 24 (34.29) 35 (50.00) −0.024 0.81
Pou2F3 (1%) 7 (10.00) 1 (1.43) 62 (88.57) −0.118 0.23
PD-L1 (1%) 2 (2.86) 0.000 68 (97.14) 0.013 0.89
PR (5%) 33 (47.14) 12 (17.14) 25 (35.71) 0.017 0.87
SALL4 (25%) 15 (21.43) 9 (12.86) 46 (65.71) −0.048 0.62
SMARCA4 (25%) 24 (34.29) 17 (24.29) 29 (41.43) −0.017 0.86
Villin (10%) 11 (15.71) 49 (70.00) 10 (14.29) −0.016 0.87
WT-1 (1%) 10 (14.29) 3 (4.29) 57 (81.43) −0.044 0.66
ZEB1 (10%) 12 (17.14) 0.000 58 (82.86) 0.053 0.59
β-catenin (50%) 20 (28.57) 15 (21.43) 35 (50.00) 0.145 0.14

For molecular expression, the percentage in parentheses represents the threshold for positive and negative expressions. CTC, circulating tumor cell; TTF-1, thyroid transcription factor-1; CK, cytokeratin; C-MET, cellular-mesenchymal to epithelial transition factor; LCA, leucocyte common antigen; MTAP, methylthioadenosine phosphorylase; ER, estrogen receptor; PD-L1, programmed cell death ligand 1; PR, progesterone receptor; SALL4, spalt-like transcription factor 4; SMARCA4, SWI/SNF-related, matrix-associated, actin-dependent regulator of chromatin; WT-1, Wilms tumor 1; ZEB1, zinc finger E-box binding homeobox 1.


Discussion

Despite tertiary medical institutions paying increased attention to CTCs as a valuable auxiliary method to detect lung cancer and predict patient prognosis, robust data on the prevalence of and factors affecting CTCs in distinct pathological subtypes of lung cancer are limited. We conducted a large retrospective cohort of lung cancer patients who received surgery with horizontal multi-clinical records. We found that CTC presents in different subsets of lung cancer, and may be regulated by distinct clinical and genomic features. Our findings support previous findings that CTC is significantly associated with imaging characteristics and covers a comprehensive clinical-genomic landscape (10,11). These results suggest that it may be beneficial to add plasma CTC testing to the standard diagnostic check-up for lung cancer or to confirm the subtype before surgery in clinical settings.

Previous studies preliminarily investigated CTCs, clinical variables, and radiomics features prior to surgery, and found that the use of CTCs could increase the proportion of patients being accurately diagnosed (12,13). Such results could also increase the accuracy of a precise diagnosis and enable targeted therapy. To further enhance understandings and improve the timely prediction, CTC testing should be performed earlier as a check-up for suspected lung cancer as it was in our study.

We also identified additional clinical and imaging factors related to CTCs that have pronounced diagnostic and prognostic effects. The presence of these factors, including the tumor size, spiculation, pleural indentation, and air bronchogram, suggest that further CTC-driven protocols using blood in the clinic could be used for patient stratification and prediction.

Our study demonstrated that CTCs in different lung cancer subtypes share distinct radiological and clinical features. Cho et al. (14) reported that a larger tumor diameter, solid nodule density and nodal metastasis were associated with the incidence of NSCLC. Conversely, our results indicated that these factors were not associated with CTCs. Similar to the LUAD cohort, strong correlations were found between CTCs and air bronchogram in the LUSC cohort, which suggests that air bronchogram may be a predictive factor for CTC. It may be that LUSC and LUAD cancer cells usually spread along the alveolar wall and alveolar septa, and involve the alveolar septa. This process maintains the integrity and normal bronchial lumen of the lobules, resulting in no significant changes in gas content, and thus the formation of a more natural bronchial inflation sign. Taken together, our findings showed that radiological variables in LUAD/LUSC and CTCs may be modulated by the bronchial or alveolar lesions.

Using complete cytological morphology and rich multi-omics information, and advanced diagnostic technology, CTCs have been used to detect the EGFR mutation and guide optimal EGFR-TKI therapy for NSCLC (15,16). Similarly, we found that CTCs define a subset of lung cancers associated with the EGFR mutation. A large-scale systematic review confirmed their diagnostic value in detecting the EGFR mutation in NSCLC, and a recent study demonstrated that CTC testing can be used to eliminate wasteful therapies and side effects in lung cancer patients with longitudinal undetectable minimal residual disease (17,18). Remarkably, our study showed that CTC levels increased as the EGFR mutation increased in patients with LUAD, which indicates that high CTCs may indicate a good response to EGFR-TKIs. However, the specific mechanisms of the lung cancer bypassed or alternative pathway activation, and histological/phenotypic transformation, should be examined in further studies.

There are several limitations in this study. First, as a retrospective study, this study had a number of inherent drawbacks, such as selection bias. Second, the results are based on a Chinese population, and the applicability of the results to other populations is under investigation. Third, while the associations between CTCs and genomic expressions were explored, the specific mechanisms regulating CTCs remain uncertain. Finally, CTCs can be isolated and analyzed using a number of methods, including droplet digital PCR, beads, emulsion, amplification, and magnetics (BEAMing), and tagged-amplicon deep sequencing. Our methods were based on beads, and we did not examine the differences between our method and any other methods. Therefore, the scientific generalization requires further evaluation.


Conclusions

This study demonstrated that tumor properties, radiological features, and genomic characteristics are significantly associated with CTCs, and vary in different subtypes of lung cancer. Our findings may help to elucidate the generation mechanism and relevant major regulation molecules of CTCs. We suggest to incorporate CTC testing into routine clinical check-ups before surgery.


Acknowledgments

Funding: This study was supported by the National Natural Science Foundation of China (No. 82300068).


Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-24-955/rc

Data Sharing Statement: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-24-955/dss

Peer Review File: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-24-955/prf

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-24-955/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. This study was independently approved by the Institutional Review Board of Shanghai Pulmonary Hospital, Shanghai, China (No. K24-539Y). The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013), International Conference on Harmonization Guidelines for Good Clinical Practice and local regulations. The patients provided informed consent to participate in the 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/.


References

  1. Li W, Liu JB, Hou LK, et al. Liquid biopsy in lung cancer: significance in diagnostics, prediction, and treatment monitoring. Mol Cancer 2022;21:25. [Crossref] [PubMed]
  2. Ahn JC, Teng PC, Chen PJ, et al. Detection of Circulating Tumor Cells and Their Implications as a Biomarker for Diagnosis, Prognostication, and Therapeutic Monitoring in Hepatocellular Carcinoma. Hepatology 2021;73:422-36. [Crossref] [PubMed]
  3. Dall'Olio FG, Marabelle A, Caramella C, et al. Tumour burden and efficacy of immune-checkpoint inhibitors. Nat Rev Clin Oncol 2022;19:75-90. [Crossref] [PubMed]
  4. Su Z, Wang Z, Ni X, et al. Inferring the Evolution and Progression of Small-Cell Lung Cancer by Single-Cell Sequencing of Circulating Tumor Cells. Clin Cancer Res 2019;25:5049-60. [Crossref] [PubMed]
  5. Shimazu K, Fukuda K, Yoshida T, et al. High circulating tumor cell concentrations in a specific subtype of gastric cancer with diffuse bone metastasis at diagnosis. World J Gastroenterol 2016;22:6083-8. [Crossref] [PubMed]
  6. Capper D, Jones DTW, Sill M, et al. DNA methylation-based classification of central nervous system tumours. Nature 2018;555:469-74. [Crossref] [PubMed]
  7. Matsui S, Kagara N, Mishima C, et al. Methylation of the SEPT9_v2 promoter as a novel marker for the detection of circulating tumor DNA in breast cancer patients. Oncol Rep 2016;36:2225-35. [Crossref] [PubMed]
  8. Jiang JH, Gao J, Chen CY, et al. Circulating tumor cell methylation profiles reveal the classification and evolution of non-small cell lung cancer. Transl Lung Cancer Res 2022;11:224-37. [Crossref] [PubMed]
  9. Owen S, Lo TW, Fouladdel S, et al. Simultaneous Single Cell Gene Expression and EGFR Mutation Analysis of Circulating Tumor Cells Reveals Distinct Phenotypes in NSCLC. Adv Biosyst 2020;4:e2000110. [Crossref] [PubMed]
  10. Xing Y, Qin F, Zhai Y, et al. Association of Clinical Features of Colorectal Cancer with Circulating Tumor Cells and Systemic Inflammatory Markers. Dis Markers 2022;2022:5105599. [Crossref] [PubMed]
  11. Kontic M, Ognjanovic M, Jovanovic D, et al. A preliminary study on the relationship between circulating tumor cells count and clinical features in patients with non-small cell lung cancer. J Thorac Dis 2016;8:1029-31. [Crossref] [PubMed]
  12. Wan Z, He H, Zhao M, et al. The development and validation of a circulating tumor cells-based integrated model for improving the indeterminate lung solid nodules diagnosis. Transl Lung Cancer Res 2023;12:566-79. [Crossref] [PubMed]
  13. Zhang W, Duan X, Zhang Z, et al. Combination of CT and telomerase+ circulating tumor cells improves diagnosis of small pulmonary nodules. JCI Insight 2021;6:e148182. [Crossref] [PubMed]
  14. Cho MS, Park CH, Lee S, et al. Clinicopathological parameters for circulating tumor DNA shedding in surgically resected non-small cell lung cancer with EGFR or KRAS mutation. PLoS One 2020;15:e0230622. [Crossref] [PubMed]
  15. Mack PC, Miao J, Redman MW, et al. Circulating Tumor DNA Kinetics Predict Progression-Free and Overall Survival in EGFR TKI-Treated Patients with EGFR-Mutant NSCLC (SWOG S1403). Clin Cancer Res 2022;28:3752-60. [Crossref] [PubMed]
  16. Qiu M, Wang J, Xu Y, et al. Circulating tumor DNA is effective for the detection of EGFR mutation in non-small cell lung cancer: a meta-analysis. Cancer Epidemiol Biomarkers Prev 2015;24:206-12. [Crossref] [PubMed]
  17. Qiu B, Guo W, Zhang F, et al. Dynamic recurrence risk and adjuvant chemotherapy benefit prediction by ctDNA in resected NSCLC. Nat Commun 2021;12:6770. [Crossref] [PubMed]
  18. Luo J, Shen L, Zheng D. Diagnostic value of circulating free DNA for the detection of EGFR mutation status in NSCLC: a systematic review and meta-analysis. Sci Rep 2014;4:6269. [Crossref] [PubMed]

(English Language Editor: L. Huleatt)

Cite this article as: Zhu L, Gao E, Tomita Y, Li A, Tao B. Circulating tumor cells are associated with lung cancer subtypes: a large-scale retrospective study. Transl Lung Cancer Res 2024;13(11):3122-3138. doi: 10.21037/tlcr-24-955

Download Citation