Abstract
Purpose:
Small cell lung cancer (SCLC) is an aggressive malignancy with a poor prognosis despite initial treatment responses. This study evaluates ctDNA for monitoring disease and assessing the efficacy of first-line therapy in patients with extensive-stage SCLC (1L ES-SCLC).
Experimental Design:
In the TAZMAN trial, 31 patients with 1L ES-SCLC received standard treatment with durvalumab and etoposide plus carboplatin or cisplatin. We analyzed 228 plasma samples from 27 of 31 patients using a liquid biopsy approach to detect somatic mutations and copy-number aberrations, while also accounting for clonal hematopoiesis mutations.
Results:
Baseline ctDNA analysis detected somatic alterations in 96.3% of patients, primarily in genes like TP53 and RB1. ctDNA dynamics during early treatment showed significant reductions in variant allele frequency, confirming early but short-lived chemosensitivity. Reduction of ctDNA below the limit of detection of the assay during induction predicted patients with longer treatment duration, surpassing imaging in distinguishing these patients. ctDNA changes often anticipated disease relapse before conventional imaging, suggesting ctDNA as a more sensitive treatment efficacy marker.
Conclusions:
The study shows that early ctDNA dynamics can provide valuable insights into treatment efficacy and potential molecular relapse in 1L ES-SCLC. ctDNA can enhance treatment monitoring and potentially guide the discontinuation of ineffective therapies. Larger studies with extended next-generation sequencing panels are needed to fully understand the potential of ctDNA in SCLC management from diagnosis to treatment and recurrence surveillance.
Introduction
Small cell lung cancer (SCLC) is an aggressive malignancy associated with tobacco exposure, high metastatic potential, and poor prognosis despite initial response to treatment (1). More recently, the use of immune checkpoint inhibitors alongside platinum–etoposide (EP) has led to improvements in patient outcomes, but the overall prognosis of SCLC remains poor (2-5).
Given that few patients with SCLC undergo surgery, limited tumor tissue has historically impeded comprehensive molecular profiling, possibly slowing down novel therapy development.
ctDNA has emerged as a viable surrogate in solid tumors, offering advantages in serial, noninvasive monitoring of disease biology and therapy resistance (6-8). Its utility extends to monitoring tumor burden, refining response assessment accuracy, and enhancing sensitivity in detecting tumor heterogeneity and therapeutic resistance (6-8). Despite limited exploration of ctDNA’s clinical utility in SCLC, the high tumor shedding in this cancer type suggests promising feasibility and sensitivity for routine ctDNA monitoring (9-16).
In this study, we conducted longitudinal ctDNA profiling in patients with first-line extensive-stage SCLC (1L ES-SCLC) enrolled in the TAZMAN trial.
Materials and Methods
Study and patients
The TAZMAN trial enrolled 31 participants in the induction phase in which patients received standard-of-care treatment with durvalumab, etoposide, and either carboplatin (28 participants) or cisplatin (three participants). Twenty-two participants entered the maintenance phase; nine participants received AZD2811 in combination with durvalumab and 13 participants received durvalumab monotherapy. AZD2811 is a potent and selective inhibitor of Aurora B kinase activity which has been incorporated into a nanoparticle carrier for intravenous administration. The study was sponsored by AstraZeneca and was conducted in line with the principles of the Declaration of Helsinki, International Council for Harmonization Guideline for Good Clinical Practice, and applicable laws and regulations. The study protocol was approved by the respective regulatory authorities and research ethics committee of each participating site and was subject to ethics committee and institutional review board approvals. All participants provided written informed consent prior to enrollment. Data were collected by study investigators and analyzed by the sponsor.
ctDNA sample collection
Mandatory blood samples for plasma ctDNA were collected throughout the course of the study from 27 patients with ES-SCLC. Specifically, serial plasma samples were collected at baseline pre-treatment (screening and C1D1), 2 weeks (C1D15), 3 weeks (C2D1), 6 weeks (C3D1), 9 weeks (C4D1), 11 weeks (C4D15), 12 weeks/ beginning of maintenance (C5D1), 14 weeks (C5D15), 15 weeks (C6D1), and every 6 weeks thereafter, as well as at disease progression and/or treatment discontinuation.
Provision of ctDNA samples was mandatory for all patients who gave informed consent; samples from patients who withdrew consent or were lost to follow-up were excluded.
The ctDNA analyses presented here were exploratory.
ctDNA analysis
Double-spun plasma was isolated from whole blood (10 mL) collected in Streck Cell-Free DNA BCT tubes. Cell-free DNA (cfDNA) was extracted from ~3 mL of plasma using standard methods and quantified using a Qubit fluorometer (Thermo Fisher Scientific). Up to 100 ng of cfDNA was used for whole-genome library construction. Each library was then split and subjected to low-pass whole-genome sequencing and targeted enrichment using a custom panel of 15 genes (ctDx SCLC, Resolution Bioscience). The custom panel was an optimized version of a previously reported gene panel (12) and was designed to detect substitutions, indels, and copy-number alterations in the coding regions of 12 genes (BRAF, CREBBP, EP300, KIT, NOTCH1, NOTCH2 coding exons 5–34, NOTCH3, NOTCH4, PIK3CA, PTEN, RB1, and TP53) using Resolution Bioscience’s bias-corrected targeted hybrid capture technology as previously described (17). The panel also contains probes for detection of amplifications in the genes MYC, MYCL1, and MYCN and control probes that target selected regions in all 22 autosomes.
Matched DNA from white blood cells (WBC) was sequenced using the same custom panel and served as the control to aid identification and removal of clonal hematopoiesis (CH) of indeterminate potential and private germline variants from the cfDNA dataset used for this study.
Plasma samples were sequenced to deep coverage (median, 3,123×) to ensure high sensitivity for detecting genomic alterations. The median sequencing depth for WBC was 5,816×.
To confirm the origin of the variants (private germline, tumor derived, and CH derived), we compared serial ctDNA data with their matched WBC data.
Variants that had a variant allele frequency (VAF) of around 50% or higher in both WBC and plasma samples from the same individual were categorized as private germline. These variants also did not show any dynamic changes across serial plasma samples.
Variants that had high VAF in plasma and low VAF in WBC were classed as tumor derived and likely a result of plasma ctDNA contamination in WBC samples.
Variants that had similar VAFs in plasma and WBC, or slightly higher VAF in WBC, were classed as CH derived.
ctDNA-based molecular response
MaxVAF was defined as the highest VAF of tumor-derived mutations at baseline.
MeanVAF was defined as the average VAF of tumor-derived mutations at a given timepoint and was calculated for each baseline and on-treatment timepoint available. Only mutations present at baseline were tracked on-treatment and included in the meanVAF calculation.
Percentage change against baseline meanVAF was the default metrics for molecular response (MR) assessment. A prespecified threshold of ≥50% versus <50% decrease in meanVAF from baseline C1D1 was selected to classify molecular responders versus molecular nonresponders. This 50% cutpoint was consistent with previous work (18).
Specifically, 100% meanVAF reduction from baseline was classified as complete MR (cMR); ≥50% meanVAF reduction from baseline was classified as partial MR (pMR); <50% meanVAF reduction from baseline C1D1 was considered no MR.
For all patients except one, the baseline timepoint chosen for all analyses was C1D1. The exception was one patient who only had a screening sample available as pre-treatment timepoint.
Assessments
The progression-free survival (PFS) and objective response rate endpoints were derived (by AstraZeneca) from site investigator assessment, according to Response Evaluation Criteria in Solid Tumors (RECIST) v1.1.
Subgroups for exploratory analysis
We selected a cutoff of ≤ or >3 cycles on maintenance therapy (i.e., total treatment duration ≤ or > ~150 days) to define shorter versus longer treatment duration, respectively. This cutoff was chosen based on the median PFS observed in the CASPIAN study, which was 5.1 months for durvalumab plus EP (4).
Statistical methods
The ctDNA analysis was exploratory in nature and, as such, data were summarized using descriptive statistics. Plasma samples included in analysis were collected up until May 2022. Clinical data were analyzed using June 18, 2022, data cutoff; patients with events occurring after June 18, 2022, were censored in analyses.
The differences in median PFS between classification groups were estimated based on the Kaplan–Meier method.
Tumor response assessment on the first CT scan after the posttreatment plasma collection (C3D1) was used to determine objective response and compared with ctDNA % change.
A Spearman rank correlation was performed between baseline cfDNA/ctDNA and the sum of the longest diameter for all target lesions at baseline per RECIST.
All tests were two-sided and considered statistically significant at P value <0.05 unless specified otherwise.
Results
For this study, we developed a comprehensive, sensitive and specific liquid biopsy approach to explore whether ctDNA profiling could detect disease-associated mutations and copy-number aberrations in 228 longitudinal plasma samples from 27 patients (Fig. 1A).
Figure 1.
Genomic landscape of baseline ctDNA in 1L ES-SCLC. A, Study schematic. B, Oncoprint of genetic alterations and CNAs. Percentage prevalence is shown on the right; clinical characteristics are shown on the top. C, Circos plot depicting CNAs and log values bounded between −1 and +1 (see density histogram of log R values in Supplementary Fig. S2 for details of these bounds). Patients are ordered by maximum days on treatment, with the longest-treated patient in the outermost position. D, Distribution of VAF and each dot represents one mutation. E, Distribution of VAF per gene and each dot represents one mutation. F, Bar plots showing nucleotide change signature. cnv, copy-number variation; gDNA, genomic DNA; indel, insertion/deletion.
At baseline, 26 of 27 (96.3%) patient samples had at least one detectable somatic alteration by targeted sequencing. TP53 (96.3%) and RB1 (81.5%) were the most frequently altered genes, followed by PTEN (66.7%), PIK3CA (59.3%), and MYCL1 (33.3%). The alterations identified also included copy-number aberrations in some common SCLC-related genes, with RB1 and TP53 gene deletion being the most frequent (Fig. 1B).
There was detectable ctDNA by low-pass whole-genome sequencing in 26 of 27 (96.3%) patients at baseline (Fig. 1C; Supplementary Figs. S1 and S2). Common chromosomal alterations in SCLC such as 3q and 5p gains and 3p, 13q, and 17p losses were identified (Fig. 1C).
The VAF values at baseline were high (all mutations, range, 1.6% to 90.4%; mean, 44.6%; Fig. 1D), confirming high tumor DNA shedding in 1L ES-SCLC. The highest clonality was observed for TP53 and RB1 mutations, reflecting their dominant clonal driver gene status (Fig. 1E).
Although our gene panel size was limited (see “Materials and Methods”), the nucleotide change signatures in ctDNA at baseline matched those found in broader next-generation sequencing (NGS) investigations (10-14). These signatures were primarily C>A transversions and C>T transitions, previously associated with smoking and aging, respectively (Fig. 1F; ref. 19).
Baseline cfDNA levels, maxVAF, and meanVAF did not correlate with the baseline sum of target lesions (SOTL) diameters calculated per RECIST v1.1 criteria (Supplementary Fig. S3A). These findings could be due to the small sample size of this cohort or the potential limitation of RECIST assessment for efficacy monitoring in SCLC as rapid tumor growth and metastasis typically result in high tumor burden by disseminated and/or confluent lesions (i.e., non-target lesions not contributing to SOTL but leading to high ctDNA levels).
We found no significant association between baseline cfDNA or ctDNA and clinical parameters such as Eastern Cooperative Oncology Group performance status and the presence/absence of liver or lung metastases, except for slightly elevated ctDNA levels in patients with liver metastases (Supplementary Fig. S3B).
Baseline tumor burden by cf/ctDNA or SOTL was not significantly different among rapidly progressing patients (i.e., short treatment duration) and patients on maintenance for ≥3 cycles (i.e., long treatment duration; Supplementary Fig. S3C).
Additionally, we analyzed serial plasma specimens throughout the study to assess ctDNA’s suitability for monitoring patient response to treatment.
ctDNA dynamics confirmed early but short-lived chemosensitivity during induction, with patients achieving on average 92.2% ctDNA decrease at C1D15 (range, 29.9%–100.0%; median, %) and 90.6% ctDNA decrease at C2D1 (range, 53.3%–100.0%; median 95.5%; Fig. 2A; Supplementary Fig. S4).
Figure 2.
Longitudinal ctDNA dynamics, molecular and radiographic responses, and the prediction of survival outcomes. A, Spider plot showing percentage change in meanVAF at baseline (C1D1) and every available on-treatment sample. Each line represents a patient, color-coded by treatment duration. B, Scatter plot showing meanVAF at baseline (C1D1) and every available on-treatment sample. Each dot represents a patient, color-coded by treatment duration. C, Swimmer plot showing PFS and molecular and radiographic responses for each patient. The second patient from the bottom experienced a serious adverse event of pneumonia resulting in a fatal outcome during the first cycle of induction therapy. D, Waterfall plot of percentage changes in ctDNA meanVAF between C3D1 and C1D1. Each bar represents a patient, color-coded by RECIST response at week 6 (C3D1) and grouped by response duration. Details of maintenance treatment and AZD2811 dose reductions are indicated. E, Scatterplot of percentage changes in ctDNA meanVAF between C3D1 and C1D1. Patients are grouped by response duration: short (progressive disease or death during induction/within 3 maintenance cycles) and long (maintenance >3 cycles). Dots are color-coded by MR at C3D1. F, Kaplan–Meier curve of PFS for patients stratified by cMR or no MR/pMR at C3D1. G, Scatterplot of percentage changes in SOTL between C3 and baseline. Patients are grouped by response duration: short (progressive disease or death during induction/within 3 maintenance cycles) and long (maintenance >3 cycles). H, Kaplan–Meier curve of PFS for patients stratified by reduction in % change SOTL at week 6 according to RECIST criteria. nMR, no MR; PD, progressive disease; PR, partial response; SD, stable disease.
Among the 24 patients with ctDNA detected at both C1D1 and C1D15, meanVAF was reduced ≥50% in 95.8% (23 of 24) of patients, including 25% (six of 24) of patients in whom ctDNA was cleared (Fig. 2A-C; Supplementary Fig. S4). Comparable results were observed at C2D1 (Fig. 2A-C; Supplementary Fig. S4).
Notably, the meanVAF decrease between baseline and early on-treatment visits was statistically significant, including in patients with high ctDNA burden at baseline (Fig. 2B).
In several cases, longitudinal ctDNA analysis identified disease relapse or recurrence before the time of disease progression by conventional imaging, with mutated clones expanding up to six cycles before the time of overt relapse (Fig. 2C; Supplementary Fig. S5).
We used a standardized MR metric to quantify ctDNA level changes and correlated them with outcomes.
At the first imaging assessment (week 6/C3D1), 91% of patients were classified as partial responders by RECIST v1.1 criteria, whereas ctDNA MR showed more variability (Fig. 2D). Patients with cMR at C3D1 were more likely to maintain durable responses (>3 cycles) on maintenance and had longer PFS (Fig. 2D-F). In contrast, the reduction in SOTL diameters did not differentiate patients with longer time on treatment or PFS (Fig. 2G and H).
A pMR or no MR during induction identified patients with fast disease progression (Fig. 2D-F; Supplementary Fig. S4). This suggests that patients with persistent ctDNA levels at the end of induction may be better suited for alternative treatment options.
In this study, we sequenced matched WBC to detect non-tumor CH mutations in longitudinal cfDNA (Fig. 1A). Tumor-specific mutations were identified by subtracting WBC-derived variants from cfDNA (see “Materials and Methods”).
We found evidence of CH mutations in 28% (seven of 25) of patients with matched cfDNA and WBC (Supplementary Fig. S6A). The VAF distribution of CH variants in cfDNA was consistently <2% (range, 0.1%–1.7%). CH variants in cfDNA exhibited significantly lower VAF than tumor-specific mutations (P < 0.001; Supplementary Fig. S6B). Correlation between VAF in WBC and their corresponding VAF in cfDNA indicated similar levels of CH mutations in both biospecimens (Supplementary Fig. S6C).
TP53, a canonical CH gene but also a gene universally altered in SCLC, was mutated in WBC as were less common CH genes such as NOTCH and CREBBP (Supplementary Fig. S6D).
CH mutations could have confounded the analysis of MR in 43% (three of seven) of patients who showed evidence of CH. This impact occurred because including CH mutations in the calculation of meanVAF and related % changes skewed the MR results (Supplementary Fig. S6E). In 29% (two of seven) of patients displaying evidence of CH, CH mutations affected meanVAF calculation but did not change the patient’s MR classification. For the remaining two of seven patients with evidence of CH, CH mutations were only detected during treatment; thus, they did not affect the MR calculation, which only considers baseline variants.
Discussion
Our findings revealed that ctDNA offers sensitive, real-time insights into somatic mutations and clonal dynamics, whereas quantitative ctDNA changes, evaluated via a standardized MR metric, indicate early treatment efficacy and response duration in SCLC management. Importantly, our study also addressed confounding factors by parallel sequencing of WBC DNA, mitigating the impact of hematopoietic cell–derived CH mutations on MR determination.
Although ctDNA analysis can closely mirror tissue-based analysis, enabling detection of intratumor heterogeneity, it can also reveal differences in gene alterations compared with tissue-based profiling, such as higher PI3K pathway alteration prevalence in this dataset (Fig. 1B; Supplementary Table S1).
The study’s notable inclusion of treatment-naïve patients treated with standard of care during induction enabled full serial mutation tracking to progression, providing a detailed temporal overview of ctDNA changes correlated with radiological evaluation and response to first-line treatment.
Early assessment during induction revealed that ctDNA dynamics confirm SCLC chemosensitivity and high response rates with chemoimmunotherapy, with all evaluable patients achieving pMR or cMR after just one cycle of EP + durvalumab. Importantly, cMR during induction predicted patients likely to remain on maintenance for >3 cycles, surpassing imaging in distinguishing patients with extended treatment duration. These findings suggest that deep and sustained molecular clearance may be necessary to achieve durable clinical benefit, although further validation with more sensitive assays and larger cohorts is warranted. However, this study, to our knowledge, marks the first dataset linking ctDNA levels with treatment duration, early molecular relapse, and outcomes in 1L ES-SCLC, indicating its potential in identifying patients with inferior overall survival benefit early in therapy.
Investigating the broad tumor evolution and understanding the mechanisms of acquired resistance in these patients fell beyond the scope of our study, partly because of the limited size of our NGS panel. However, we observed evidence of a NOTCH3 variant acquired during treatment in one patient, detected at C3D1 and subsequent timepoints with VAF between 0.5% and 1.2% (Supplementary Fig. S7). This suggests that the future use of larger NGS panels or exome sequencing could potentially offer insights into the biology of resistance to chemo-immunotherapy in this setting.
In summary, our findings highlight the clinical significance of employing ctDNA profiling in personalized medicine. This approach facilitates therapeutic target identification and has the potential to transform patient care by offering less invasive liquid biopsy methods for SCLC diagnosis, risk assessment, and early disease progression detection. The predictive potential of early ctDNA dynamics in treatment response warrants attention, potentially guiding the rational discontinuation of ineffective therapies. Larger studies utilizing NGS-based ctDNA assays are required to comprehensively understand their role across the spectrum from diagnosis to treatment and recurrence surveillance.
Supplementary Material
Supplementary data for this article are available at Clinical Cancer Research Online (http://clincancerres.aacrjournals.org/).
Translational Relevance.
Longitudinal ctDNA profiling in small cell lung cancer provides a rapid, noninvasive method to monitor therapeutic response and gather insights into first-line treatment duration. Further research is required to evaluate the utility of ctDNA dynamics in personalized small cell lung cancer management, optimization of treatment strategies, and improvement of patient care and outcomes.
Acknowledgments
The authors thank the patients, their families, and caregivers. L.A.B. is supported by NIH/NCI awards: R01-CA207295, U01-CA256780, U24-CA213274, and P50-CA070907. The authors would also like to acknowledge Leanne Winzer for providing the clinical data. This study was funded by AstraZeneca.
Footnotes
Authors’ Disclosures
C. Ciardullo reports employment with AstraZeneca and ownership of AstraZeneca stock/shares. L. Tobalina reports employment with AstraZeneca and ownership of AstraZeneca stock/shares. T.H. Carr reports employment with AstraZeneca and ownership of AstraZeneca stock/shares. P.G. Szekeres reports employment with AstraZeneca and ownership of AstraZeneca and Eli Lilly and Company stock/shares. S. Kraljevic reports employment with AstraZeneca and ownership of AstraZeneca stock/shares. L.A. Byers reports grants and personal fees from AstraZeneca, AbbVie, and Amgen and personal fees from Arrowhead Pharmaceuticals, BeiGene, Boehringer Ingelheim, Chugai Pharma, Daiichi Sankyo, Genentech, Jazz Pharmaceuticals, Merck Sharp & Dohme, Novartis, and Puma Biotechnology outside the submitted work, as well as a patent 11732306 issued to NA. G. Fabbri reports employment with AstraZeneca and ownership of AstraZeneca stock/shares.
Data Availability
Data underlying the findings described in this article may be obtained in accordance with AstraZeneca’s data sharing policy described at https://astrazenecagrouptrials.pharmacm.com/ST/Submission/Disclosure. Data for studies directly listed on Vivli can be requested through Vivli at www.vivli.org. Data for studies not listed on Vivli could be requested through Vivli at https://vivli.org/members/enquiries-about-studies-not-listed-on-the-vivli-platform/. The AstraZeneca Vivli member page is also available, outlining further details at https://vivli.org/ourmember/astrazeneca/.
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Associated Data
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Supplementary Materials
Data Availability Statement
Data underlying the findings described in this article may be obtained in accordance with AstraZeneca’s data sharing policy described at https://astrazenecagrouptrials.pharmacm.com/ST/Submission/Disclosure. Data for studies directly listed on Vivli can be requested through Vivli at www.vivli.org. Data for studies not listed on Vivli could be requested through Vivli at https://vivli.org/members/enquiries-about-studies-not-listed-on-the-vivli-platform/. The AstraZeneca Vivli member page is also available, outlining further details at https://vivli.org/ourmember/astrazeneca/.