Yu Feng and Yutao Liu contributed equally to this work.
This study aimed to investigate the feasibility of biomarkers based on dynamic circulating tumor DNA (ctDNA) to classify small cell lung cancer (SCLC) into different subtypes.
Tumor and longitudinal plasma ctDNA samples were analyzed by next-generation sequencing of 1,021 genes. PyClone was used to infer the molecular tumor burden index (mTBI). Pre-treatment tumor tissues (T1) and serial plasma samples were collected (pre-treatment [B1], after two [B2], six [B3] cycles of chemotherapy and at progression [B4]).
Overall concordance between T1 and B1 sequencing (n=30) was 66.5%, and 89.5% in the gene of
Monitoring ctDNA based
Lung cancer remains the leading cause of cancer death worldwide [
To better instruct clinical practice in the treatment of SCLC, numerous attempts have been made to investigate the molecular subtypes of SCLC. For example, four major subtypes of SCLC are defined based on the expression of ASCL1, NEUROD1, POU2F3, or YAP1, and some other related subtypes are subsequently derived, which accelerates the research on subtype-specific treatment approaches [
In recent years, circulating tumor DNA (ctDNA) sequencing technology has maturely developed. It could be used as a supplementary method to monitor tumor burden, improve the accuracy of response assessment, and increase the detection sensitivity of non-measurable or occult metastases [
Here, we performed comprehensive next-generation sequencing on baseline tumor tissue samples and serial ctDNA samples from SCLC patients treated with first-line systematic therapy. We validated the feasibility of monitoring
A multi-center, single-arm, case series translational research prospectively enrolled patients with histologically confirmed SCLC at three medical centers. All patients received first-line etoposide 100 mg/m2 (days, 1–3) plus cisplatin 75 mg/m2 (days, 1–3) every 3 weeks for 4–6 cycles (drug adjustment, radiotherapy and surgery were allowed on the basis of patients’ condition). Maintenance therapy was optional for patients with stable disease (SD), partial response (PR), or complete response (CR) after first-line treatment until disease progression (PD), unacceptable adverse reactions, or withdrawal from clinical studies. Eligible patients were 18–75 years old regardless of sex; had at least one measurable target lesion at baseline according to Response Evaluation Criteria in Solid Tumors ver. 1.1 (RECIST v 1.1); had an Eastern Cooperative Oncology Group (ECOG) performance status of 0 or 1 (on a 5-point scale, with higher numbers reflecting poorer physical conditions); without receiving previous systemic therapy for SCLC. The disease stage was not limited, but patients were excluded if they had untreated symptomatic central nervous system metastases, or if they had any other uncontrolled serious diseases. The primary endpoint was PFS, as assessed by investigators, among patients in the intention-to-treat population. Key secondary endpoints included OS, the objective response rate (ORR, defined as the total percentage of CR and PR) and disease control rate (DCR, defined as the total percentage of CR, PR and SD) which was evaluated by investigators every two cycles of chemotherapy according to RECIST v1.1. Total tumor size was defined as the sum of longest axial diameters of all measurable lesions (short axial diameters of lymph nodes) via computed tomography (CT) or magnetic resonance imaging (MRI) according to the RECIST v 1.1.
Pre-treatment tumor biopsy specimens (T1) and longitudinal plasma samples (pre-treatment [B1], after two [B2], six [B3] cycles of chemotherapy and at progression [B4]) were collected to perform the tumor-normal matched next-generation sequencing of 1,021 cancer-related genes, which enables the simultaneous detection of single-nucleotide variants (SNVs), small insertions/deletions (InDels), structural variants (SVs), and copy-number variants (CNVs).
This study aimed to investigate the clinical value of ctDNA to predict the efficacy and prognosis and monitor disease during treatment in SCLC patients. All patients provided written informed consent before participation in the study.
Peripheral blood was collected in Streck tubes and separated by centrifugation at 2,500 ×g for 10 minutes, and then transferred to microcentrifuge tubes and centrifuged at 16,000 ×g for another 10 minutes to remove remaining cell debris. Lymphocytes from the first centrifugation step were used for the extraction of germline genomic DNA. The gDNA of lymphocytes and tissue samples were extracted using the DNeasy Blood & Tissue Kit (Qiagen, Hilden, Germany). Circulating cell-free DNA was isolated using QIAamp Circulating Nucleic Acid Kit (Qiagen). DNA concentration was measured using a Qubit fluorometer (Invitrogen, Carlsbad, CA) and the Qubit dsDNA HS (high sensitivity) Assay Kit (Invitrogen). The size distribution of the cfDNA was assessed using an Agilent 2100 BioAnalyzer and the DNA HS kit (Agilent Technologies, Santa Clara, CA). All the procedures were performed according to the manufacturer’s instructions. Sequencing libraries of both cfDNA and gDNA were constructed with the KAPA DNA Library Preparation Kit (Kapa Biosystems, Wilmington, MA) according to the manufacturer’s protocol. Libraries were hybridized to custom-designed biotinylated oligonucleotide probes (Integrated DNA Technologies, Coralville, IA). Capture probe was designed to cover coding sequencing or hot exons of 1,021 genes frequently mutated in solid tumors, including 14 genes with therapeutic value that recommended by the National Comprehensive Cancer Network (NCCN) guidelines or approved by the Food and Drug Administration (FDA), 220, 98 and 689 genes with therapeutic, diagnostic or prognostic value based on well-powered studies, multiple small studies, and small studies or a few case reports, respectively (
Sequencing data were analyzed using default parameters. After removal of terminal adaptor sequences and low-quality data, reads were mapped to the reference human genome (hg19) and aligned using BWA (0.7.12-r1039). SNVs were called using MuTect (ver. 1.1.4) and NChot, a software developed in-house to review hotspot variants. Small insertions and deletions (InDels) were called by GATK. Somatic copy-number alterations were identified with CONTRA (v2.0.8). Copy number variations (CNV) was expressed as the ratio of adjusted depth between ctDNA and germline DNA. SVs were identified with NCsv (in house). Mutations were considered a candidate somatic mutation only when (1) the mutation was detected in at least 5 high-quality reads containing the particular base, (2) the mutation was not present in > 1% of the population in the 1000 Genomes Project (version phase 3) or dbSNP databases (The Single Nucleotide Polymorphism Database, version dbSNP 137), and (3) the mutation was not present in a local database of normal samples. High-quality reads were selected with Phred score ≥ 30, mapping quality ≥ 30, and a lack of paired-end reads bias. For tumor tissue and ctDNA somatic mutations, the mutant must be present in ≥ 1% and 0.5% of reads, respectively. The candidate variants were all manually verified in the Integrative Genomics Viewer. The median values of average effective depth of coverage for tissue samples and ctDNA samples were 841× and 2,670.5×, respectively.
PyClone, a statistical model based on a Bayesian clustering method [
Survival was calculated by Kaplan-Meier method and compared using the Mantel-Cox log-rank test. Chi-square test was used to investigate the impact of baseline characteristics on response rate. Univariate and multivariate Cox regression was used to analyze the impact of baseline characteristics on survival. Fisher’s exact test was used to explore the dynamic changes of
From November 2018 to September 2020, a total of 38 SCLC patients were enrolled in this study (
As shown in
Previous study has demonstrated that a high rate of mutations (median, 94%) detected in tissue samples were also detected in matched ctDNA samples, which however remains to be further verified due to the very limited sample size (n=8) [
We first analyzed the impact of baseline characteristics including age, sex, disease stage, smoking status, and status of frequent gene mutations, on the efficacy of first-line chemotherapy, in which no factor was found associated with ORR or PFS (
We then divided the patients into three subtypes according to the dynamic changes of somatic mutation of
To further confirm the predictive availability of the dynamic changes of somatic
We first analyzed the association between dynamic change of variant allele frequency (VAF) of
Patients with matched B1 and B2 ctDNA samples (n=30) presented decreased mTBI at B2, same trend was seen for tumor sizes (
We then analyzed the mPFS and OS of the 11 patients with earlier mTBI elevation at B3 before PD based on whether chose maintenance treatment (n=6) or not (n=5) after first-line therapy, and no difference was observed (log rank p > 0.05) (
We first explored the potential relationship of mTBI and distant metastases at initial diagnosis. The median value of mTBI in 11 patients with distant metastasis (IVA or IVB) at diagnosis was significantly higher than that of the remaining 24 patients (40.18 vs. 15.8, p=0.03) (
Furthermore, we explored the relationship between dynamic changes of mTBI (B4 compared with B1) and new metastases at progression (defined as new tumor lesions during or after treatment). A total of 14 patients with B1 and B4 ctDNA samples were divided into two subgroups (subgroup A, patients with increased mTBI values at B4 than B1; subgroup B, patients with decreased mTBI values at B4 than B1). Seven patients (n=7, 100%) in subgroup A (
One interesting case in subgroup A caught our attention (
This study provided clinical and genetic evidences for non-invasively tumor monitoring during treatment, revealing that early dynamic changes of ctDNA-based
Due to the limited number of operable patients [
In SCLC patients, identification of prognostic indicators through baseline genomic profiling is always challenging. Attempts to find predictors of treatment efficacy and prognosis using ctDNA samples at baseline (B1) in this study were not so satisfactory. We further focused on longitudinal molecular changes to distinguish SCLC patients with different prognosis. We creatively designed an easily performed method to classify SCLC patients, which could early predict tumor response, recurrence pattern, and survival time by using dynamic changes of ctDNA based
Levels of ctDNA in plasma correlate with tumor burden had been exhibited in plenty of studies through different perspectives [
Due to the potential damage induced by imaging examinations and economic reasons, brain MRI would not be performed at every follow-up visit for patients without brain metastases at initial diagnosis in China. In addition, lesions less than 10 mm are difficult to identify by radiographic imaging, which is of vital importance for therapeutic response assessment [
Our study showed a limitation that we enrolled both limited and extensive stage of SCLC patients simultaneously, and the limited stage occupied with a higher percentage of 68.6%. This selective bias might lead to the incomplete reflection of the real-world data of SCLC, and different treatment modalities between them might produce some potential bias of outcomes, which couldn’t be completely avoided. However, we had explored the effect of tumor staging on tumor efficacy, neither the ORR, mPFS, nor the mOS was associated with the tumor stage based on the Veterans Administration Lung Study Group (VALG) staging scheme in our cohort (p > 0.05). More importantly, the aim of our study is to investigate the biomarkers based on dynamic ctDNA sequencing, SCLC patients were all detected ctDNA in pre-treatment peripheral blood samples, irrespective of the limited or extensive stage, which could also reduce the impact of staging bias. Another limitation of our study was the small sample size, but the number of SCLC patients who were monitored using serial ctDNA sequencing in our cohort was still more than other similar studies [
In conclusion, serial ctDNA sequencing provided a clinically reliable and feasible approach to explore biomarkers to predict the treatment efficacy, recurrence patterns (sensitive, refractory and resistant) and survival outcomes of first-line therapy in patients with SCLC, especially the dynamic changes of
Supplementary materials are available at Cancer Research and Treatment website (
All procedures were conducted in accordance with the Declaration of Helsinki. This study was approved by the ethics committee of National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (Beijing, China; approved No. 18-151/1729). This study was registered at the Chinese Clinical Trial Registry as ChiCTR1900023956.
Conceived and designed the analysis: Feng Y, Liu Y, Yuan M, Wang Z, Hu X.
Collected the data: Feng Y, Liu Y, Yuan M, Dong G, Zhang H, Zhang T, Zhu H, Xing P, Wang H, Shi Y, Hu X.
Contributed data or analysis tools: Feng Y, Liu Y, Yuan M, Wang Z, Hu X.
Performed the analysis: Feng Y, Liu Y, Yuan M, Chang L, Xia X, Li L, Wang Z, Hu X.
Wrote the paper: Feng Y, Liu Y, Wang Z, Hu X.
Conflict of interest relevant to this article was not reported.
The authors thank the patients who participated in the study, the staff members at the study sites, and the staff members who were involved in data collection and analyses. This study was supported by the National Key Research and Development Project (2019YFC1315704), CAMS Innovation Fund for Medical Sciences (2017-I2M-1-005), the National Natural Sciences Foundation (81871889, 82072586) and Beijing Natural Science Foundation (7212084).
Mutational concordance between tumor DNA and circulating tumor DNA (ctDNA) sequencing. (A) Somatic mutation profiles of paired tumor and ctDNA samples. (B) The number of shared, tissue only, blood only mutations and concordance rate for each individual. (C) Venn diagrams demonstrated the concordance rate between tumor tissue and ctDNA sequencing in terms of all mutations and mutations in
Detection rate of
The performance of molecular tumor burden index (mTBI) with computed tomography (CT) for evaluating therapeutic response. (A) Evaluation of therapeutic response in 18 patients using mTBI were consistent with CT. (B) Progressive disease was identified earlier using mTBI than by using tumor size. (C) Evaluations in two patients were inconsistent between mTBI and CT.
Changing in circulating tumor DNA and imaging during progressive disease in patients P35. CT (computed tomography): Imaging shows new metastases on right adrenal gland and enlargement of primary lesion. mTBI: Values of molecular tumor burden index at B1, B2 and B4, respectively. C, cycle of first-line chemotherapy; VAF, variant allele frequency.
Clinical characteristics of the study population
Characteristic | Value (n=35) |
---|---|
61 (43–69) | |
Male | 25 (71.4) |
Female | 10 (28.6) |
Never smoker | 8 (22.9) |
Former smoker | 27 (77.1) |
0 | 19 (54.3) |
1 | 16 (45.7) |
Limited stage | 24 (68.6) |
Extensive stage | 11 (31.4) |
Yes | 24 (68.6) |
No | 11 (31.4) |
Yes | 1 (2.9) |
No | 34 (97.1) |
Yes | 11 (31.4) |
No | 24 (68.6) |
Yes | 13 (37.1) |
No | 22 (62.9) |
Complete response | 0 |
Partial response | 28 (80.0) |
Stable disease | 6 (17.1) |
Progressive disease | 1 (2.9) |
Refractory | 7 (20.0) |
Resistant | 5 (14.3) |
Sensitive | 23 (65.7) |
Values are presented as median (range) or number (%). ECOG, Eastern Cooperative Oncology Group.
Maintenance therapy include apatinib, etoposide soft capsule, sintilimab plus anlotinib, or the initial etoposide plus cisplatin regimen,
In terms of the recurrence pattern, sensitive was defined as disease progression ≥ 90 days after first-line platinum-based chemotherapy, resistant as disease progression < 90 days and refractory as during first-line chemotherapy.
Prediction of different molecular characteristics on clinical prognostic characteristics
Positive | Negative | SE (95% CI) | SP (95% CI) | PPV (95% CI) | NPV (95% CI) | |
---|---|---|---|---|---|---|
Tumor response | ||||||
PR | 23 | 2 | 92 (80.6–103.4) | 80 (24.5–135.5) | 95.8 (87.2–104.5) | 66.7 (12.5–120.9) |
SD | 1 | 4 | ||||
Recurrence pattern | ||||||
Sensitive | 19 | 0 | 100 | 54.5 (19.5–89.6) | 79.2 (61.6–96.7) | 100 |
Refractory and resistant | 5 | 6 | ||||
Tumor response | ||||||
PR | 17 | 8 | 68 (48.3–87.7) | 80 (24.5–135.5) | 94.4 (82.7–106.2) | 33.3 (2.0–64.6) |
SD | 1 | 4 | ||||
Recurrence pattern | ||||||
Sensitive | 14 | 5 | 73.7 (51.9–91.5) | 63.6 (29.7–97.5) | 77.8 (56.5–99.1) | 58.3 (25.6–91.1) |
Refractory and resistant | 4 | 7 | ||||
New metastasis | ||||||
Yes | 7 | 0 | 100 | 85.7 (50.8–120.7) | 87.5 (57.9–117.1) | 100 |
No | 1 | 6 |
CI, confidence interval; mTBI, molecular tumor burden index; NPV, negative predicting value; PPV, positive predicting value; PR, partial response; SD, stable disease; SE, sensitivity; SP, specificity.
The prediction of tumor response and recurrence pattern by the dynamic changes of single gene mutation of
The prediction of tumor response and recurrence pattern by the dynamic changes of single gene mutation of
The predicting value of new metastasis by mTBI (B4-B1).