Circulating tumour DNA analysis for early detection of lung cancer: a systematic review

Introduction

Lung cancer is characterised by its high incidence and poor overall prognosis. It ranks among the top cancer types in both incidence and mortality in different parts of the world (1,2). Much effort has been devoted to improving the survival outcome of patients with lung cancer, including development of early detection methods. The basis for early detection of lung cancer, similar to other types of cancer, is the better survival noted among early-stage disease. The five-year survival rate for localised lung cancer is about 60%, but only less than 10% for metastatic diseases. Unfortunately, only 40% of lung cancer cases are diagnosed as localised or regional diseases at presentation. This highlights an unmet need for improving early detection of localised diseases (1).

Circulating tumour DNA (ctDNA) refers to DNA released into the circulation/blood from cancer cells and could be used to inform genetic and epigenetic alterations present in the tumour (3). There has been intense research on ctDNA analysis for screening, prognostication and surveillance in cancer diagnostics (4). Specifically for the screening purpose, the minimal invasiveness and simple logistic arrangement of a blood draw required for ctDNA analysis provides a readily available screening biomarker (5). In contrast, for screening of lung cancer, low dose computed tomography (LDCT) is another option but remains underutilised despite evidence suggesting survival benefits through radiological screening among high-risk individuals (6,7). Utility of ctDNA for early detection of lung cancer has been explored in a number of studies, including those which focused on lung cancer only or pan-cancer detection (8,9). Given the highly variable degrees of evidence supporting use and adoption of ctDNA analysis, we have conducted this systematic review to evaluate the available evidence on various methods of ctDNA assessment for screening of lung cancer.

Biology of ctDNA

Before the systematic review analysis, the fundamental concepts of ctDNA (and plasma DNA in general) are to be discussed. Plasma DNA is a mixture of DNA fragments released from different organs or cell types [as a result of apoptosis or necrosis (10)]. Therefore, identifying ctDNA at the background of plasma DNA released from other normal cell and tissue types would pose a challenge on sensitivity, especially for small tumours in the early detection setting when the level of ctDNA is expected to be low (11).

Cancer-associated somatic mutation and aberrant methylation are the two most studied molecular features of ctDNA in cancer diagnostics (12,13) (Figure 1). Detection of somatic mutations in plasma is aimed at cancer-derived mutation. However, there is a lack of common hotspot mutations for virtually all cancer types (including lung cancer). Therefore, multiple targets (over multiple genes) have been included with an attempt to improve the detection sensitivity (8,14). The specificity of somatic mutations detected in plasma DNA as surrogates of tumour-derived DNA is another issue. These somatic mutations may be derived from conditions other than tumour cells, such as clonal hematopoiesis of indeterminate potential (CHIP) (15). CHIP describes the expansion of a subclone of haemopoietic cells which carry leukemogenic mutations. Though associated with an increased risk of hematological malignancies, it is yet to be defined as malignant. It is common among the elderly population with a prevalence of up to 10% among adults older than 70 years old. Cancer-associated aberrant methylation is another frequently explored ctDNA marker. Compared to somatic mutation analysis, methylation profiling of plasma DNA could yield the tissue-of-origin information (16,17), which would largely facilitate the establishment of a blood-based pan-cancer screening test (18,19). Methylation analysis forms the core technology of the now commercially available multi-cancer early detection (MCED) test branded as Galleri^TM test by Grail, Inc.

Figure 1 Different molecular analyses of circulating tumour DNA for early detection of lung cancer.

As mentioned, plasma DNA exists as fragments of DNA in the circulation (20,21). The size distribution of plasma DNA exhibits a modal peak of ~160 bp that corresponds to a mononucleosomal size (21). Specifically, ctDNA is suggested to be shorter than its normal counterparts in plasma (21). The realization of non-random fragmentation has fuelled research on the fragmentation biology and the simultaneous exploration of ‘fragmentomics’ markers with diagnostics potentials (Figure 1). These markers are signatures of differential fragmentation of ctDNA and DNA derived from normal cells possibly because of the underlying pathophysiological process. They include fragment size (22,23), fragment ends (24), end motifs (25) and jagged ends (26) and all of them have demonstrated potential clinical utilities in a cancer model.

Analytical methods and pre-analytical considerations

Differentiating ctDNA from non-tumor circulating DNA requires detection of cancer-associated genetic, epigenetic and/or fragmentomic alterations as discussed above. Digital polymerase chain reaction (dPCR) and massively parallel sequencing (MPS), also known as next generation sequencing (NGS), are the most commonly employed methods (27). Adaptations to the techniques enable detection of the properties of ctDNA.

Each of the molecular techniques, dPCR and NGS, has its own advantages and disadvantages. dPCR offers precise and sensitive detection of variants even at low allelic fractions, and its downstream analysis and interpretation are relatively straightforward. However, it relies on prior knowledge of the presence of variants in the tumor, and it is less efficient in detecting multiple variants in a single assay (27,28). These limitations hinder the utility of dPCR in early cancer detection where the presence of analytical targets is unknown. In contrast, NGS enables multiplexing, allowing the simultaneous detection of multiple variants within or across samples. This enables untargeted or semi-targeted detection of multiple variants at a lower average cost. Nonetheless, NGS requires more complex downstream bioinformatic analysis, longer analysis time, and higher upfront instrumental costs (29).

Pre-analytical factors also impose a significant impact on the results. These factors include types of blood tubes used, post-collection handling of specimens and delay which could affect the degree of ex-vivo release of DNA from blood cells into plasma (30). In addition to the above-mentioned factors, method of cfDNA isolation would influence relative abundance of ctDNA within samples, with subsequent impacts on diagnostic performance on the assays (31). We present this article in accordance with the PRISMA reporting checklist (available at https://atm.amegroups.com/article/view/10.21037/atm-23-1572/rc).

Methods

Literature search

We performed a systematic review on publications of PubMed before 1 January 2023. Search terms were “(cell-free DNA (cfDNA) OR circulating tumour DNA (ctDNA)) AND (lung cancer) AND ((screening) OR (detection) OR (diagnosis))”. Only publications in English were reviewed. Titles and abstracts were screened and full-texts were reviewed when applicable. We also screened all referenced articles of the selected studies. Studies which evaluated ctDNA analysis for lung cancer detection in symptomatic/asymptomatic patients with no known previous history of lung cancer (primary but not recurrent cancer) were included. Abstracts were reviewed by the authors independently. Discrepancies were resolved with discussion and reaching a consensus.

Data analysis

Information on the study design and the inclusion and exclusion criteria of the selected studies were reviewed. For lung cancer cases, the cancer stage and histologic subtype information were retrieved. For control cases, the clinical status information including smoking habits, chronic obstructive pulmonary disease (COPD) status and presence of lung nodules were retrieved, if available.

Clinical end points

For evaluation of diagnostic performance, the primary outcome of interest was the ability of ctDNA analysis in differentiating patients with lung cancer from individuals without lung cancer, including the sensitivity, specificity, positive and negative predictive values and area under the curve value in receiver operating characteristics curve analysis. For the majority of diagnostic studies, tissue biopsy served as the reference method for determination of cancer statuses in participants.

Quality assessment

Quality assessment for the identified diagnostic studies was performed with the Quality Assessment of Diagnostic Accuracy Studies-2 tool (32).

Results

Study selection is shown in the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) flowchart in Figure 2. Among the 797 articles identified in the database search, 56 were included in the systematic review. A summary table on the ctDNA properties, study cohort (study size, target population and control group), methodology and findings was provided (Table 1).

Figure 2 Preferred Reporting Items for Systematic Reviews and Meta-Analyses flow diagram on the study selection. ctDNA, circulating tumor DNA.

Study design

A case-control study design was adopted in all except one study in the evaluation of the diagnostic performance of ctDNA analysis for detection of lung cancer. In the remaining study, the group prospectively analyzed ctDNA variants and circulating protein biomarkers [by CancerSEEK (75)] for pan-cancer detection including lung cancer (9). Among these case-control studies, the majority included samples of both early- (stage I–II) and advanced- (stage III–IV) stage cancer, except for a few that targeted early-stage (or stage I) cancer only (44,47,72,80). There were 39 studies that were designed for detection of lung cancer only, and 17 studies for pan-cancer early detection. Different histological subtypes of lung cancer had been included in the cancer cohorts. However, the performance of the various ctDNA analyses was not separately reported for the different lung cancer subtypes in most studies (53,68). The control groups used in the studies were also heterogenous, while some studies included ‘healthy’ controls (37,66,70,78) and other studies included individuals with a benign lung nodule or other pulmonary diseases, e.g., COPD (56,67,81). Such comparison with benign lung disease cases could be used to address the utility of ctDNA analysis under certain clinical scenarios, such as management of patients with lung nodules of unknown nature (56,67). Given the variability in case selection (distribution of early versus advanced cases) and the control groups used, it would be difficult to directly compare the diagnostic performances of the different ctDNA assays for lung cancer detection.

ctDNA properties and analytical methods

Various molecular features of cell-free DNA (cfDNA) were utilized to differentiate lung cancer and non-cancer samples. Quantification of cfDNA concentration was employed in 7 studies (33,34,56,67,69,73,74); cfDNA mutation was employed in 17 studies (8,9,14,35-43,73-77); cfDNA methylation in 29 (Table 1); cfDNA hydroxymethylation in 5 (62-66); and cfDNA fragmentation profiles in 8 (66,70-72,78-80,83). Among the selected studies, 17 studies utilised different combinations of the above cfDNA molecular features and/or cfDNA concentration and/or circulating protein biomarkers (8,9,14,33,34,56,63,66-69,73-78).

For detection of cfDNA mutations (8,9,14,35-43,73-77), most studies have targeted multiple genes (usually driver genes) in order to enhance sensitivity for lung cancer detection. In contrast to mutation analysis, methylation analysis of cfDNA offers the advantage to inform the tissue of origin based on the cancer type- and/or tissue-specific methylation profile and therefore favours the development of a pan-cancer early detection test (18). The performance of mutation-based or methylation-based analysis have been evaluated in both types of studies specifically designed for lung cancer and for pan-cancer detection. In general, for both modalities, lower sensitivities were reported for detection of early-stage cancer, which were below 50%. In a large-scale, case-control, pan-cancer detection study based on cfDNA methylation analysis, the sensitivities for stage I and II lung cancer were about 20% and 50%, and those for stage III and IV cancer were over 80%, at a specificity of 99.3% (validation data) (19).

As mentioned, ‘fragmentomics’ analysis of cfDNA is based on the pathophysiologically associated fragmentation patterns of cfDNA and it has been explored for cancer diagnostics (20). Such cfDNA fragmentomics analysis have also been evaluated for lung cancer detection (66,70-72,78-80,83). Cristiano et al. (70) and Mathios et al. (71). have developed a prediction score, known as the DELFI (DNA evaluation of fragments for early interception) score, based on fragmentation size and coverage characteristics in windows throughout the genome, as well as chromosomal arm and mitochondrial DNA copy numbers. Using the DELFI prediction score, they could achieve a sensitivity of about 60% and a specificity of about 80% for detection of early-stage (stage I and II) lung cancer in an independent validation cohort (70,71).

In order to enhance the diagnostic performance, some researchers have explored the potential synergistic effect of combining the analyses of different molecular features of cfDNA, circulating protein biomarkers and clinical features (8,9,14,33,34,56,63,66-69,73-78). In one study, Liu et al. demonstrated that the integrated model which incorporated the analyses of cfDNA mutation, methylation and protein biomarkers achieved the highest diagnostic performance than individual models with a single type of biomarker (8). Similarly, in the prospective liquid biopsy-based pan-cancer screening study, the cancer cases (not only limited to lung cancer) identified were either ctDNA biomarker-positive or protein biomarker-positive except one case (9). This finding might suggest the advantage of a multi-modality testing approach. At the same time, it would also be interesting to study the difference in tumour characteristics (84), if any, between those that are ctDNA biomarker positive versus protein-biomarker positive.

Complementary diagnostic role with low-dose computed tomography (LDCT)

LDCT was proven to be an effective screening tool for lung cancer but remained underutilized (6,7). The clinical role of liquid biopsy to complement LDCT for lung cancer screening might be partly addressed in one recently published study by Mathios et al. (71) and another aforementioned prospective pan-cancer screening study (9). In the prospective pan-cancer screening study, 9,911 asymptomatic participants with no known history of malignancies had received blood testing involving 9 protein biomarkers and cfDNA targeted sequencing for mutation analysis. In addition to the 3 cancer cases picked up by LDCT (unknown total number of participants who underwent LDCT), 9 more lung cancer cases (1 at stage I; 2 at stage II; 2 at stage III and 4 at stage IV) were identified by the blood test (9). In another study by Mathios et al. (71), the researchers have modelled the performance of incorporating ctDNA analysis (based on plasma DNA fragmentation and also clinical information) to select high-risk subjects for subsequent LDCT. These attempts were aimed to improve the low adherence rate of LDCT. All these would suggest the potential complementary role of ctDNA-based (or liquid biopsy-based) test to the standard modality for cancer screening.

Emerging applications

Leveraging the ability of NGS in untargeted, multiplex detection of ctDNA, multi-cancer detection represents a reasonable and promising next step in the development of the technology. For instance, Klein et al. (18) and Liu et al. (19) employed detection of methylation markers in cfDNA for detection and differentiation of tissue of origin, with diagnostic performances comparable to single-cancer detection. This represents an attractive approach which improves cost-effectiveness via simultaneous detection of multiple malignancies.

Discussion

In cancer management, applications of ctDNA analysis such as therapeutic prediction and disease monitoring (in cases with known driver mutations) have already been adopted for routine clinical uses (85-87). However, challenges remain in early detection of lung cancer without common driver mutations, in which defining a tumour-specific genetic/epigenetic alteration as a biomarker would be more costly and less readily available. Ascertaining their clinical utilities necessitates large-scale clinical studies and integration with the diagnostic and/or management flow, which require significant time and resources. Nevertheless, ctDNA analysis could potentially address the unmet clinical need of early detection in lung cancer. This has prompted us to conduct this systematic review to evaluate the performance of the different ctDNA analysis techniques reported for lung cancer detection.

As reviewed in this systematic review, the design of studies is very heterogenous. The high heterogeneity may affect the generalizability of the studies’ conclusions to routine clinical settings. Although the objectives of most studies were aimed to evaluate ctDNA for early detection of lung cancer, the study populations of some studies were usually made up of large proportions of cases at advanced stages, while some studies did not specify the proportion of cases at each clinical stage. Tests tend to be more accurate at differentiating patients at advanced stages from healthy subjects. Performance metrics calculated from a population with the majority having advanced diseases tend to be an over-estimate when applied to screening populations, where a large proportion of subjects are healthy or at early stages of disease.

Various methodologies have been used for detection and quantitation of specific molecular features of ctDNA. cfDNA methylation/hydroxymethylation profiling and mutation detection in a panel of genes represent approaches unlimited to exclusive detection of cancers with specific driver mutations. The differing approaches confer different sensitivities and specificities to the analyses, compromising comparability between studies and generalizability of their conclusions. As the field evolves and the repertoire of analytical techniques expands, complexity in the issue is expected to further increase and further validation across different populations would be required. In addition, ctDNA analysis is highly influenced by pre-analytical conditions as mentioned, including blood tubes, storage conditions, DNA extraction methods etc. Standardization and cross-method comparisons would be required to confirm the performance as reported (88-90).

Similarly, studies have utilized various bioinformatics tools to detect the mutational status of target genes and the methylation status of target genes or genomic regions. In addition, various machine learning models have been built to differentiate lung cancer patients and healthy subjects based on various cfDNA properties. High sensitivity and specificity were reported in some of the studies. However, the majority of these studies had relatively small training cohorts. A machine learning model generated from training groups of a larger scale with external validation would better reflect the performance in separating lung cancer patients from both healthy subjects and subjects with other cancer types.

Studies have also investigated the utility of cfDNA analysis for detection of multiple types of cancers (9,18,19,39,43,48,53,59,62,65,69,70,72,74,75,81,82). While this could represent an approach that is expected to be more cost-effective, further studies are needed to address the manner in which the testing could be integrated to current workflows.

One study limitation is that only one search engine is included for the search of eligible studies, though most studies that analysed the different ctDNA markers should have been covered.

Conclusions

ctDNA demonstrates great potential for early detection of lung cancer. While various analytical methodologies have shown promise for clinical translation, validation in prospective cohorts is necessary to confirm the performance. This would help defining the target screening population and integration into clinical care.

Acknowledgments

Funding: This work was supported by the Innovation and Technology Commission of the Hong Kong SAR Government (InnoHK initiative).

Footnote

Provenance and Peer Review: This article was commissioned by the Guest Editor (Calvin S. H. Ng) for the series “Lung Cancer Management—The Next Decade” published in Annals of Translational Medicine. The article has undergone external peer review.

Reporting Checklist: The authors have completed the PRISMA reporting checklist. Available at https://atm.amegroups.com/article/view/10.21037/atm-23-1572/rc

Peer Review File: Available at https://atm.amegroups.com/article/view/10.21037/atm-23-1572/prf

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://atm.amegroups.com/article/view/10.21037/atm-23-1572/coif). The series “Lung Cancer Management—The Next Decade” was commissioned by the editorial office without any funding or sponsorship. W.K.J.L. has filed multiple patents on cell-free DNA analyses for cancer diagnostics. W.K.J.L. is a member of the Data Advisory Committee of the Hong Kong Genome Institute. W.K.J.L. is a Board member of DRA and hold equities in Illumina. P.J. holds equities in Grail/Illumina, KingMed Future and Take2. PJ is a consultant to KingMed Future and receives consulting fees. P.J. is Director of DRA, Take2, KingMed Future, and Insighta. P.J. has filed various patents or patent applications related to cell-free DNA and receives royalties from Illumina, Take2, DRA, and KingMed Future. The authors have no other conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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

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