Skip to main content

DNAfusion: an R/Bioconductor package for increased sensitivity of detecting gene fusions in liquid biopsies



EML4-ALK gene fusions are oncogenic drivers in non-small cell lung cancer (NSCLC), and liquid biopsies containing EML4-ALK fragments can be used to study tumor dynamics using next-generation sequencing (NGS). However, the sensitivity of EML4-ALK detection varies between pipelines and analysis tools.


We developed an R/Bioconductor package, DNAfusion, which can be applied to BAM files generated by commercially available NGS pipelines, such as AVENIO. Forty-eight blood samples from a training cohort consisting of 41 stage IV EML4-ALK-positive NSCLC patients and seven healthy controls were used to develop DNAfusion. DNAfusion detected EML4-ALK in significantly more samples (sensitivity = 61.0%) compared to AVENIO (sensitivity = 36.6%). The newly identified EML4-ALK-positive patients were verified using droplet digital PCR. DNAfusion was subsequently validated in a blinded validation cohort comprising 24 EML4-ALK-positive and 24 EML4-ALK-negative stage IV NSCLC patients. DNAfusion detected significantly more EML4-ALK individuals in the validation cohort (sensitivity = 62.5%) compared to AVENIO (sensitivity = 29.2%). DNAfusion demonstrated a specificity of 100% in both the training and validation cohorts.


Here we present DNAfusion, which increases the sensitivity of EML4-ALK detection in liquid biopsies and can be implemented downstream of commercially available NGS pipelines. The simplistic method of operating the R package makes it easy to implement in the clinical setting, enabling wider expansion of NGS-based diagnostics.

Peer Review reports


Gene fusions are important oncogenic drivers in lung cancer, and the discovery of new fusions is ongoing [1]. For non-small cell lung cancer (NSCLC), this has led to the development of tyrosine kinase inhibitors (TKIs) specifically targeting the resulting fusion proteins, including ALK [2], ROS1 [3], RET [4], and NTRK [5]. Until now, these gene fusions have been identified in tissue biopsies [6], but the recent development of liquid biopsies offers the possibility to study tumor genetics in plasma samples [7]. In NSCLC, gene fusions involving ALK are the most comprehensively studied, and several TKIs targeting ALK-fusions are available for the treatment of patients with ALK positive NSCLC [8]. The most common ALK fusion partner is EML4, and EML4-ALK has been studied extensively in liquid biopsies. However, different next-generation sequencing (NGS) approaches and downstream software solutions have resulted in varying sensitivity for the detection of EML4-ALK in circulating tumor DNA (ctDNA). Sensitivity varies between 30 and 80% [9,10,11,12,13,14,15,16,17,18] depending on the choice of NGS and the bioinformatic pipeline. Gene fusions can be identified using commercially available hybridization capture-based methods, including AVENIO (Roche) [12, 19, 20], Guardant360 (GuardantHealth) [10, 16], and InVisionFirst (Inivata) [13]. Although these pipelines are simple to implement in the clinic and the output can be interpreted easily, they suffer from reduced sensitivity compared to results generated with, for example, Tophat2 [21] and FACTERA [22], which on the other hand require considerable bioinformatic knowledge to be implemented in routine diagnostics.

Here we present the R package, DNAfusion (available through Bioconductor), which can be implemented downstream of NGS pipelines to increase the sensitivity of EML4-ALK detection. DNAfusion is available across computer platforms through R. It is easy to install and use, making it applicable for clinical research and diagnostics. Here we compare the results of DNAfusion with the AVENIO output; however, it should be noted that BAM files generated with any hybridization capture-based paired-end sequencing can be used as input in DNAfusion.



DNAfusion utilizes paired-end sequencing to discover EML4-ALK fragments in liquid biopsies, as described in Fig. 1. Following EML4-ALK gene fusion, cfDNA fragmentation results in gene fragments from EML4 and ALK, as well as some fragments spanning the fusion breakpoint. Hybridization capture of ALK fragments (e.g., in the AVENIO pipeline) also captures fragments with EML4 fused to the ALK gene. After paired-end sequencing, DNAfusion identifies EML4-ALK fusion fragments by isolating soft-clipped EML4 reads with a mate position in ALK (for methodological details se Methods). DNAfusion can characterize the gene fusion by identifying the breakpoint position and the DNA sequences around the breakpoint in both EML4 and ALK. Furthermore, DNAfusion determines the read depth at the breakpoint in EML4. This serves as a surrogate for the ctDNA load in the blood sample, which in turn reflects the tumor burden [23,24,25]. Monitoring the read depth at the EML4 breakpoint provides the possibility to monitor treatment efficacy because a reduction in ctDNA levels early during therapy is correlated with positive treatment response, including progression-free survival and overall survival [26]. Examples of two EML4-ALK-positive BAM files identified with DNAfusion are displayed in Additional file 1: Fig. S1.

Fig. 1
figure 1

Identification of EML4-ALK variants using hybridization capture NGS and DNAfusion. EML4 and ALK localized on chr2 can make a gene fusion. During cell-free DNA (cfDNA), release to the bloodstream the DNA is fragmented into approximately 165 bp fragments. The hybridization capture of ALK fragments will isolate ALK fragments but also EML4-ALK fusion fragments. Paired-end sequencing will generate reads aligning to EML4 and ALK with reads spanning the fusion point becoming soft clipped. DNAfusion detects EML4 reads with a mate in ALK and soft-clipped reads spanning the fusion breakpoint are identified. The bases leading up to the EML4 breakpoint and following the ALK breakpoint are determined. Created with

Comparing detection of ALK fusions with the AVENIO software and DNAfusion

We created DNAfusion in order to increase the sensitivity of detecting EML4-ALK fragments, without affecting the specificity, in liquid biopsies following sequencing with the AVENIO pipeline. We therefore compared the outputs of the AVENIO Oncology Analysis software with the DNAfusion output (Fig. 2).

Fig. 2
figure 2

Comparing DNAfusion with AVENIO in the training cohort. a Number of EML4 clipped reads determined with DNAfusion in cell lines. b Number of patients with detectable and undetectable EML4-ALK in liquid biopsies, determined with either AVENIO or DNAfusion (n = 41). c Number of patients who are EML4-ALK positive or negative determined with DNAfusion in patients who are EML4-ALK negative with AVENIO. Patients are grouped according to their ctDNA status apart from EML4-ALK as determined with AVENIO. d The EML4 breakpoint read depth in patients positive for EML4-ALK in both AVENIO and DNAfusion (n = 15) or only DNAfusion (n = 10). Median and IQR are indicated. e Number of healthy individuals with detectable or undetectable EML4-ALK in plasma, determined with either AVENIO or DNAfusion (n = 7). f Number of positive droplets for EML4-ALK in patients identified as EML4-ALK positive with DNAfusion but not with AVENIO. *P < 0.05

First, we evaluated the ability of DNAfusion to detect EML4-ALK in NSCLC cell lines. We tested the two EML4-ALK-positive cell lines, H3122 and H2228 [27], and the EML4-ALK-negative cell lines, A549 and HCC827. In Fig. 2a, the number of identified clipped EML4 reads representing EML4-ALK reads is displayed for each cell line. Only EML4-ALK cell lines had detectable clipped reads. We then compared the AVENIO software with DNAfusion on the MonAlec cohort (Fig. 2b, Additional file 2: Table S1). DNAfusion identified significantly more EML4-ALK-positive patients compared to AVENIO (Fisher’s exact test: P = 0.046). The addition of DNAfusion increased the sensitivity from 36.6% (15/41) to 61.0% (25/41). All EML4-ALK-positive patients identified with AVENIO were also identified with DNAfusion (Additional file 2: Table S1). The AVENIO pipeline will identify mutations in 197 frequently mutated genes in NSCLC. We wanted to investigate whether the presence of additional mutations besides EML4-ALK fusions in the plasma (ctDNA positive) was associated with the increased DNAfusion sensitivity. We classified the EML4-ALK-negative patients determined with AVENIO (n = 26) as either ctDNA negative (n = 12) or ctDNA positive (n = 14) (Additional file 2: Table S2) and determined their EML4-ALK status with DNAfusion. As displayed in Fig. 2c, DNAfusion sensitivity is not dependent on the presence or absence of ctDNA. We tested the read depth at the EML4 breakpoint for the patients identified as positive either with both AVENIO and DNAfusion or with DNAfusion only (Fig. 2d). Unsurprisingly, the read depth was significantly higher (Mann–Whitney test, P = 0.021) for the 15 patients identified with both AVENIO and DNAfusion compared to DNAfusion only (n = 10). This indicates that DNAfusion can detect EML4-ALK fusions with fewer EML4-ALK reads compared to AVENIO, although there is also an overlap between the read depths of the two groups. To address the specificity of DNAfusion we tested seven healthy controls and did not detect any EML4-ALK in any of the individuals (specificity = 100%, Fig. 2e). Of the 10 EML4-ALK-positive patients identified with DNAfusion but not with AVENIO, five patients had plasma available and were tested with droplet digital PCRC (ddPCR). Based on the output of DNAfusion identifying the EML4 and ALK sequences surrounding the breakpoint, patient-specific primer and probe sets were designed (Additional file 2: Table S3, Additional file 1: Fig. S2). EML4-ALK-positive droplets were identified in all five plasma samples (Fig. 2f). It should be noted that only a single EML4-ALK droplet was detected in MONA_17, but three or more droplets were detected for the remaining four patients.

Validation of DNAfusion

To verify the findings in Fig. 2, we combined NGS outputs from a previous study by Madsen et al. (n = 24) [28], of EML4-ALK positive patients, and NGS outputs from another study by Clement et al. (n = 24) [29], of EGFR-mutated but EML4-ALK-negative patients, to make a joint validation cohort (n = 48). The BAM files were blinded, meaning that the data interpreter did not know which of the two cohorts the plasma sample was from during the DNAfusion analysis. The results of the validation cohort are displayed in Fig. 3.

Fig. 3
figure 3

DNAfusion and AVENIO results of the validation cohort. a Number of patients with detectable and undetectable EML4-ALK in liquid biopsies from the Madsen cohort, determined with either AVENIO or DNAfusion (n = 24). b Number of patients with detectable and undetectable EML4-ALK in liquid biopsies from the Clement cohort, determined with either AVENIO or DNAfusion (n = 24). c The EML4 breakpoint read depth in patients positive for EML4-ALK in both AVENIO and DNAfusion (n = 6) or only DNAfusion (n = 8). Median and IQR are indicated. D EML4-ALK positivity for AVENIO and DNAfusion for the Madsen cohort patients before the first line (n = 14) or second/third line (n = 10) ALK treatment. e Number of EML4 clipped reads detected with either AVENIO or DNAfusion throughout the follow-up of Madsen_18 and Madsen_15. *P < 0.05; **P < 0.01

Similar to the results in Fig. 2b, Fig. 3a demonstrates that DNAfusion detected significantly more EML4-ALK patients (15/24, sensitivity = 62.5%) compared to AVENIO (7/24, sensitivity = 29.2%, Fisher’s exact test, P = 0.042, Additional file 2: Table S4). Additionally, analysis of the blinded negative controls of the Clement cohort demonstrated a specificity of 100% for both AVENIO and DNAfusion (Fig. 3b). Like the results of the MonAlec cohort, the EML4-ALK-positive patients only detected with DNAfusion had a lower read depth than the patients identified with both AVENIO and DNAfusion (Fig. 3c). All of the patients in the MonAlec cohort had their blood samples drawn before initiation of ALK TKI therapy, but 10/24 patients in the Madsen cohort had initiated ALK TKI therapy before NGS analysis. None of these patients were identified as EML4-ALK positive with AVENIO analysis; however, DNAfusion could detect EML4-ALK in four of these patients, which demonstrates the ability of DNAfusion to detect minimal residual ctDNA during TKI therapy. Furthermore, DNAfusion also detected more EML4-ALK-positive patients in the treatment-naïve cohort (Fig. 3d). Two patients from the Madsen cohort (patient Madsen_18 and Madsen_15) were identified as EML4-ALK-negative with AVENIO at the time of the first NGS, whereas an EML4-ALK fusion was detected in a later blood sample. Importantly, the breakpoint position in EML4 detected in the baseline blood sample with DNAfusion, was identical to the breakpoint position identified with AVENIO in a later blood sample (Fig. 3e). This demonstrates how DNAfusion can detect EML4-ALK at lower amounts of ctDNA which is crucial for the detection of disease relapse during treatment. In addition, the third blood sample analyzed for Madsen_18 demonstrates a complete clearance of the EML4-ALK fusion with AVENIO; however, DNAfusion detects residual EML4-ALK mutant molecules.


The AVENIO pipeline is a commercially available NGS workflow where the output is easy to interpret for researchers without bioinformatic knowledge. However, we found that AVENIO has reduced sensitivity for the detection EML4-ALK in liquid biopsies [12, 28], compared to other more refined bioinformatic tools [10, 13, 15, 17]. We therefore developed DNAfusion, an easy-to-use and simple to implement tool, to be used downstream of the AVENIO pipeline that could increase the sensitivity. Through R, DNAfusion works fast (Additional file 1: Fig. S3) and across computer platforms, making it suitable for flexible implementation in the clinical setting.

A limitation of the presented study is that the results are solely based on NGS with the AVENIO pipeline, however DNAfusion will work with any capture-based NGS of cfDNA. Furthermore, the study is limited by the small EML4-ALK-positive (n = 65) and EML4-ALK-negative (n = 31) cohort sizes. The limited cohort size could result in over- or underestimation of the sensitivity and specificity for DNAfusion compared to AVENIO. Although DNAfusion identifies EML4-ALK in more plasma samples than the AVENIO pipeline, these findings need to be validated in a larger cohort using different NGS pipelines.

Applying AVENIO and DNAfusion provides the opportunity to monitor the ctDNA dynamics of both gene fusions as well as other somatic mutations. At the time of writing this paper DNAfusion has been developed to target EML4-ALK fusions, but we plan to expand the number of ALK fusion partners in the future. Furthermore, we plan to develop a similar approach toward other gene fusions, such as ROS1 [3], RET [4], and NTRK [5] in NSCLC, but also gene fusions identified in other solid cancers, such as TMPRSS2-ERG in prostate cancer [30], EVT6-NTRK3 in secretory breast carcinoma [31] etc.

In Fig. 3e, it is demonstrated how DNAfusion can detect EML4-ALK molecules earlier than AVENIO. The fact that AVENIO detects the same EML4-ALK fusion breakpoint in a later blood sample from the same patient demonstrates how the EML4-ALK fusion detected with DNAfusion in an earlier blood sample is not a false positive result based on a low number of reads. This result clearly shows how DNAfusion can detect EML4-ALK fusions in a blood sample with a lower ctDNA load than AVENIO. Furthermore, the third NGS of Madsen_18 analyzed with AVENIO misclassifies the patient as a clearer of the EML4-ALK fusion; however, re-analysis with DNAfusion detected residual EML4-ALK molecules classifying the patient as a non-clearer. This is clinically relevant information given that ctDNA clearance predicts the outcome during TKI treatment [18, 32, 33] and illustrates how DNAfusion is more sensitive in classifying patients as clearers or non-clearers.


In this study, we present DNAfusion, an R/Bioconductor package for detection of EML4-ALK fusions with NGS. In both the training cohort and the validation cohort, DNAfusion displayed higher sensitivity than the AVENIO algorithm regarding the detection of EML4-ALK molecules and maintained a specificity of 100%. The straightforward implementation of DNAfusion can help expand the utility of NGS-based diagnostics in the clinic and guide treatment decisions.


Patient cohorts

Eighty-nine stage IV NSCLC patients and seven healthy controls were included in this study (Additional file 1: Fig. S4). Sixty-five patients were EML4-ALK positive, of which 41 are part of the ongoing prospective MonAlec study (NCT04708639) [34] and 24 were part of a previous EML4-ALK cohort published by Madsen et al. [28] (Called the Madsen cohort, Additional file 2: Table S4). ALK rearrangements were tested as part of the routine diagnostics using fluorescence in situ hybridization (FISH) or NGS analysis of tissue samples. The seven healthy controls were used as EML4-ALK negative controls alongside 24 NSCLC patients from the EGFR positive cohort (NCT02284633) published by Clement et al. [29] (called the Clement cohort).

Blood samples

Blood samples were collected in 10 mL K2EDTA (BD vacutainer, Becton, Dickinson and Company, Franklin Lakes, NJ, USA) tubes, however, for the MonAlec study the blood was drawn in 10 mL Cell-free DNA BCT tubes (Streck Corporate, La Vista, NE, USA). For the Clement and MonAlec cohorts, all baseline blood samples were drawn before treatment initiation. For the Madsen cohort, 14/24 patients were TKI treatment naïve, and 10 had received prior TKI therapy at the baseline blood sample collection [28]. The plasma was isolated within 2 h of extraction by centrifugation at 1900g for 10 min and subsequently stored at − 80 °C. When thawed, the plasma was centrifuged again at 16,000g for 10 min.

Cell lines

NSCLC cell lines with EML4-ALK [27], H2228 (CRL-5935, ATCC, LCG standards, Wesel, Germany), and H3122 (Adi F. Gazdar, UT Southwestern, Dallas, TX, USA), or without EML4-ALK, HCC827 (CRL-2868, ATCC, LCG standards, Wesel, Germany), and A549 (CCL-185, ATCC, LCG standards, Wesel, Germany), were grown in an RPMI medium with 10% fetal calf serum and 1% penicillin–streptomycin solution. All cells were cultivated in 5% CO2 at 37 °C. Approximately 2.0 × 106 cells were lysed in 100 µL lysis buffer (Tris–HCl 50 mM, EDTA 10 mM, NP-40 1%, SDS 1%) and physically fragmented by sonication to an average fragment length of 200–500 bp. The DNA was treated with 20 µg Proteinase K and purified using the NucleoSpin Gen and PCR Clean-up kit (Macherey–Nagel, Dueren, Germany). The purified DNA was kept at − 20 °C until applied to NGS.

Cancer personalized profiling by deep sequencing (CAPP-seq)

Cell-free DNA (cfDNA) was isolated from 2.0 to 4.0 mL plasma using the AVENIO cfDNA Isolation Kit (Roche Sequencing Solutions, Mannheim, Germany). cfDNA concentrations were estimated with the Qubit dsDNA HS Assay Kit (Thermo Fisher Scientific, Waltham, MA, USA), and the fragment lengths were analyzed using Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). NGS libraries were prepared with the AVENIO ctDNA Library Prep Kit (Roche Sequencing Solutions, Mannheim, Germany) and AVENIO ctDNA Surveillance Panel (Roche Sequencing Solutions, Mannheim, Germany), covering 197 genes, or the AVENIO ctDNA Expanded panel (Roche Sequencing Solutions, Mannheim, Germany) for the Madsen cohort, covering 77 genes. The gene fragments were sequenced using 100 paired-end sequencing on NextSeq 500 (Illumina, San Diego, CA, USA). The BAM files and the AVENIO mutation call were made using the AVENIO Oncology Analysis software.

Droplet digital PCR (ddPCR)

Droplet digital PCR (ddPCR) experiments were run in duplicates using the QX200 AutoDG Droplet Digital PCR System (Bio-Rad, Hercules, CA, USA). Each well containing 11 µL of ddPCR Supermix for Probes (no UTP), 1 µL of forward and reverse primer (20 µM), 1 µL of fluorescent probe (1 µM), and 8 µL of DNA sample to a total volume of 22 µL. The details of primers and probes are available in Additional file 2: Table S3. Droplets were generated using the QX200 AutoDG (Bio-Rad, Hercules, CA, USA), and the PCR was performed using a GeneAmp PCR System 9700 (Applied Biosystems, Waltham, MA, USA). Droplets were read using the QX200 Droplet Reader (Bio-Rad, Hercules, CA, USA), and the data were analyzed in QX Manager 1.2 Standard Edition.


The seven healthy controls and the MonAlec cohort (Additional file 2: Table S1) were used as a training set for DNAfusion. The Clement and Madsen (Additional file 2: Table S4) cohorts were combined and used as a separate validation cohort. The BAM files from the Madsen and Clement cohorts were blinded during analysis with DNAfusion enabling unbiased interpretation of the output. The position deduplicated BAM files were used as input for DNAfusion from all individuals. The EML4_ALK_detection() function was used to determine whether the plasma sample was EML4-ALK positive. EML4_ALK_detection() takes reads aligned to hg38 or hg19 and filters reads from EML4 (chr2: 42169353-42332548 for hg38). Next, EML4 reads with a paired read in ALK (chr2: 29192774-29921586 for hg38) is filtered. A threshold for the minimum number of EML4-ALK read pairs needed can be set (default = 2). Next, soft-clipped reads are identified as the reads with an “S” in the cigar string. The aligned part of these reads represents the DNA sequence (identified using EML4_sequence()) leading up to the EML4-breakpoint (identified using break_position()) whereas the soft-clipped part represents the DNA sequence (identified using ALK_sequence()) following the ALK-breakpoint. The minimum number of soft-clipped reads needed for the sample to be EML4-ALK positive, is set to 2 as default. If the EML4_ALK_detection() function identifies EML4-ALK it returns a GAlignments object containing EML4 soft-clipped reads with a paired read in ALK. If EML4-ALK is not identified the GAlignments object is empty. DNAfusion was run in R version 4.2.1 and is available at


The ability for DNAfusion and the AVENIO software to detect EML4-ALK DNA fusions were compared using Fisher’s exact test. Differences in read depths were tested using a Mann–Whitney test. A two-sided P < 0.05 was considered significant. Statistical tests were performed in GraphPad Prism v. 9.3.1.

Availability of data and materials

BAM files from CAPP-seq of cell lines have been deposited in the European Nucleotide Archive (ENA) at EMBL-EBI under accession number PRJEB56814 ( DNAfusion is available at and runs on Linux, MAC OS, and Microsoft-Windows. It requires R (version ≥ 4.2.0), which is available at and Bioconductor (version ≥ 3.16) available at



Cancer personalized profiling by deep sequencing


Cell-free DNA


Circulating tumor DNA


Droplet digital PCR


Fluorescence in situ hybridization


Next-generation sequencing


Non-small cell lung cancer


Tyrosine kinase inhibitor


  1. Mertens F, Johansson B, Fioretos T, Mitelman F. The emerging complexity of gene fusions in cancer. Nat Rev Cancer. 2015;15(6):371–81.

    Article  CAS  PubMed  Google Scholar 

  2. Peters S, Camidge DR, Shaw AT, Gadgeel S, Ahn JS, Kim DW, et al. Alectinib versus crizotinib in untreated ALK-positive non-small-cell lung cancer. N Engl J Med. 2017;377(9):829–38.

    Article  CAS  PubMed  Google Scholar 

  3. Drilon A, Siena S, Dziadziuszko R, Barlesi F, Krebs MG, Shaw AT, et al. Entrectinib in ROS1 fusion-positive non-small-cell lung cancer: integrated analysis of three phase 1–2 trials. Lancet Oncol. 2020;21(2):261–70.

    Article  CAS  PubMed  Google Scholar 

  4. Gainor JF, Curigliano G, Kim DW, Lee DH, Besse B, Baik CS, et al. Pralsetinib for RET fusion-positive non-small-cell lung cancer (ARROW): a multi-cohort, open-label, phase 1/2 study. Lancet Oncol. 2021;22(7):959–69.

    Article  CAS  PubMed  Google Scholar 

  5. Hong DS, DuBois SG, Kummar S, Farago AF, Albert CM, Rohrberg KS, et al. Larotrectinib in patients with TRK fusion-positive solid tumours: a pooled analysis of three phase 1/2 clinical trials. Lancet Oncol. 2020;21(4):531–40.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Bruno R, Fontanini G. Next generation sequencing for gene fusion analysis in lung cancer: a literature review. Diagnostics (Basel). 2020;10(8):521.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Rodríguez J, Avila J, Rolfo C, Ruíz-Patiño A, Russo A, Ricaurte L, et al. When tissue is an issue the liquid biopsy is nonissue: a review. Oncol Ther. 2021;9:89–110.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Remon J, Pignataro D, Novello S, Passiglia F. Current treatment and future challenges in ROS1- and ALK-rearranged advanced non-small cell lung cancer. Cancer Treat Rev. 2021;95:102178.

    Article  CAS  PubMed  Google Scholar 

  9. Liu L, Liu H, Shao D, Liu Z, Wang J, Deng Q, et al. Development and clinical validation of a circulating tumor DNA test for the identification of clinically actionable mutations in nonsmall cell lung cancer. Genes Chromosom Cancer. 2018;57(4):211–20.

    Article  CAS  PubMed  Google Scholar 

  10. Supplee JG, Milan MSD, Lim LP, Potts KT, Sholl LM, Oxnard GR, et al. Sensitivity of next-generation sequencing assays detecting oncogenic fusions in plasma cell-free DNA. Lung Cancer. 2019;134:96–9.

    Article  PubMed  Google Scholar 

  11. Horn L, Whisenant JG, Wakelee H, Reckamp KL, Qiao H, Leal TA, et al. Monitoring therapeutic response and resistance: analysis of circulating tumor DNA in patients with ALK+ lung cancer. J Thorac Oncol. 2019;14(11):1901–11.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Christopoulos P, Dietz S, Angeles AK, Rheinheimer S, Kazdal D, Volckmar AL, et al. Earlier extracranial progression and shorter survival in ALK-rearranged lung cancer with positive liquid rebiopsies. Transl Lung Cancer Res. 2021;10(5):2118–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Mezquita L, Swalduz A, Jovelet C, Ortiz-Cuaran S, Howarth K, Planchard D, et al. Clinical Relevance of an amplicon-based liquid biopsy for detecting ALK and ROS1 fusion and resistance mutations in patients with non-small-cell lung cancer. JCO Precis Oncol. 2020;4:272–82.

    Article  Google Scholar 

  14. Kunimasa K, Kato K, Imamura F, Kukita Y. Quantitative detection of ALK fusion breakpoints in plasma cell-free DNA from patients with non-small cell lung cancer using PCR-based target sequencing with a tiling primer set and two-step mapping/alignment. PLoS ONE. 2019;14(9):e0222233.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Cui S, Zhang W, Xiong L, Pan F, Niu Y, Chu T, et al. Use of capture-based next-generation sequencing to detect ALK fusion in plasma cell-free DNA of patients with non-small-cell lung cancer. Oncotarget. 2017;8(2):2771–80.

    Article  PubMed  Google Scholar 

  16. Leighl NB, Page RD, Raymond VM, Daniel DB, Divers SG, Reckamp KL, et al. Clinical utility of comprehensive cell-free DNA analysis to identify genomic biomarkers in patients with newly diagnosed metastatic non-small cell lung cancer. Clin Cancer Res. 2019;25(15):4691–700.

    Article  CAS  PubMed  Google Scholar 

  17. Wang Y, Tian PW, Wang WY, Wang K, Zhang Z, Chen BJ, et al. Noninvasive genotyping and monitoring of anaplastic lymphoma kinase (ALK) rearranged non-small cell lung cancer by capture-based next-generation sequencing. Oncotarget. 2016;7(40):65208–17.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Kwon M, Ku BM, Olsen S, Park S, Lefterova M, Odegaard J, et al. Longitudinal monitoring by next-generation sequencing of plasma cell-free DNA in ALK rearranged NSCLC patients treated with ALK tyrosine kinase inhibitors. Cancer Med. 2022;11(15):2944–56.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Newman AM, Bratman SV, To J, Wynne JF, Eclov NC, Modlin LA, et al. An ultrasensitive method for quantitating circulating tumor DNA with broad patient coverage. Nat Med. 2014;20(5):548–54.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Verma S, Moore MW, Ringler R, Ghosal A, Horvath K, Naef T, et al. Analytical performance evaluation of a commercial next generation sequencing liquid biopsy platform using plasma ctDNA, reference standards, and synthetic serial dilution samples derived from normal plasma. BMC Cancer. 2020;20(1):945.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Kim D, Pertea G, Trapnell C, Pimentel H, Kelley R, Salzberg SL. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 2013;14(4):R36.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Newman AM, Bratman SV, Stehr H, Lee LJ, Liu CL, Diehn M, et al. FACTERA: a practical method for the discovery of genomic rearrangements at breakpoint resolution. Bioinformatics. 2014;30(23):3390–3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Diehl F, Li M, Dressman D, He Y, Shen D, Szabo S, et al. Detection and quantification of mutations in the plasma of patients with colorectal tumors. Proc Natl Acad Sci USA. 2005;102(45):16368–73.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Lam VK, Zhang J, Wu CC, Tran HT, Li L, Diao L, et al. Genotype-specific differences in circulating tumor DNA levels in advanced NSCLC. J Thorac Oncol. 2021;16(4):601–9.

    Article  CAS  PubMed  Google Scholar 

  25. Winther-Larsen A, Demuth C, Fledelius J, Madsen AT, Hjorthaug K, Meldgaard P, et al. Correlation between circulating mutant DNA and metabolic tumour burden in advanced non-small cell lung cancer patients. Br J Cancer. 2017;117(5):704–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Wang H, Zhou F, Qiao M, Li X, Zhao C, Cheng L, et al. The role of circulating tumor DNA in advanced non-small cell lung cancer patients treated with immune checkpoint inhibitors: a systematic review and meta-analysis. Front Oncol. 2021;11:671874.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Koivunen JP, Mermel C, Zejnullahu K, Murphy C, Lifshits E, Holmes AJ, et al. EML4-ALK fusion gene and efficacy of an ALK kinase inhibitor in lung cancer. Clin Cancer Res. 2008;14(13):4275–83.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Madsen AT, Winther-Larsen A, McCulloch T, Meldgaard P, Sorensen BS. Genomic profiling of circulating tumor DNA predicts outcome and demonstrates tumor evolution in ALK-positive non-small cell lung cancer patients. Cancers (Basel). 2020;12(4):947.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Clement MS, Ebert EBF, Meldgaard P, Sorensen BS. Co-occurring MET amplification predicts inferior clinical response to first line erlotinib in advanced stage EGFR-mutated NSCLC patients. Clin Lung Cancer. 2021;22:e870–7.

    Article  CAS  PubMed  Google Scholar 

  30. Wang Z, Wang Y, Zhang J, Hu Q, Zhi F, Zhang S, et al. Significance of the TMPRSS2:ERG gene fusion in prostate cancer. Mol Med Rep. 2017;16(4):5450–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Tognon C, Knezevich SR, Huntsman D, Roskelley CD, Melnyk N, Mathers JA, et al. Expression of the ETV6-NTRK3 gene fusion as a primary event in human secretory breast carcinoma. Cancer Cell. 2002;2(5):367–76.

    Article  CAS  PubMed  Google Scholar 

  32. Ebert EBF, McCulloch T, Hansen KH, Linnet H, Sorensen B, Meldgaard P. Clearing of circulating tumour DNA predicts clinical response to first line tyrosine kinase inhibitors in advanced epidermal growth factor receptor mutated non-small cell lung cancer. Lung Cancer. 2020;141:37–43.

    Article  PubMed  Google Scholar 

  33. Boysen Fynboe Ebert E, McCulloch T, Holmskov-Hansen K, Linnet H, Sorensen B, Meldgaard P. Clearing of circulating tumour DNA predicts clinical response to osimertinib in EGFR mutated lung cancer patients. Lung Cancer. 2020;143:67–72.

    Article  PubMed  Google Scholar 

  34. Meldgaard P. Monitoring alectinib treatment by detection of ALK translocations in serial blood samples from non-small cell lung cancer patients (MonAlec), NCT04708639 https://clinicaltrials.gov2021 [Circulating tumor DNA can be used to monitor the treatment effect and identify developing resistance mutations during ALK directed TKI treatment.].

Download references


We thank Birgit Westh Mortensen for the excellent technical assistance. The authors are grateful for all the patients who participated in this study.


This project was supported by Vilhelm Pedersen and Hustrus Legat.

Author information

Authors and Affiliations



CTM and BSS conceived and designed the study. CTM and ERA analyzed the data. CTM wrote the code of DNAfusion, and ERA contributed with inputs to DNAfusion. ERA performed the ddPCR experiments. MPU and PM contributed with clinical data and patient material. CTM wrote the original draft of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Boe Sandahl Sorensen.

Ethics declarations

Ethics approval and consent to participate

All patients gave informed written consent in accordance with the Declaration of Helsinki. The study of the patients in the Madsen cohort was approved by the Central Denmark Region Committees on Biomedical Research Ethics (no. 1-10-72-266-15). The study of the patients in the Clement cohort was approved by the National Committee on Health Research Ethics (no. 1-10-72-83-14) and the Danish Data Protection Agency (no. 1-16-02-431-14). The study of the patients in the MonAlec cohort was approved by the National Committee on Health Research Ethics (no. 1-10-72-37-19). Healthy individuals were anonymized and gave informed consent.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1.

Supplementary figures 1–4.

Additional file 2.

Supplementary tables 1–4.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Maansson, C.T., Andersen, E.R., Ulhoi, M.P. et al. DNAfusion: an R/Bioconductor package for increased sensitivity of detecting gene fusions in liquid biopsies. BMC Bioinformatics 24, 131 (2023).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: