Abstract
Introduction
Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of metastatic melanoma, but a percentage of patients did not show benefit. Circulating tumor DNA (ctDNA) has emerged as a potential non-invasive tool for monitoring disease evolution and treatment response. The present study aimed to evaluate the clinical utility of ctDNA dynamics in patients with metastatic melanoma receiving ICIs, while exploring its role in the oncological course.
Materials and methods
The LIQUID-MEL study is a prospective, single-centre pilot study including patients with BRAF/NRAS-mutant metastatic melanoma. ctDNA was quantified using digital droplet PCR (ddPCR) at four different time points. Uni- and multivariable Cox regression models were used to assess the correlation between shedding and progression-free survival (PFS), and overall survival (OS).
Results
Overall, 23 patients were included. At baseline, ctDNA was detectable in 5/23 (21.7 %) cases. Baseline ctDNA shedding was associated with shorter PFS (3.88 months vs. 0.69 months, p=0.012). A strong numerical trend was observed also in OS (12.66 months vs. 2.53 months, p=0.287). Shedding at baseline did not demonstrate independent prognostic or predictive value in the uni- and multivariable analysis. The longitudinal analysis revealed intriguing patterns of ctDNA shedding in individual patients.
Conclusion
ctDNA detectability and its dynamic changes during treatment may have potential clinical utility in patients with metastatic melanoma, offering a valuable non-invasive tool for monitoring disease and treatment response. The small sample size limited the statistical power of the analysis. Further studies with larger cohorts are needed to validate its role in routine clinical practice.
Keywords: Melanoma, ctDNA, Immune checkpoint inhibitors, Prognosis, Liquid biopsy, Monitoring
1. Introduction
Long-term outcomes of patients with metastatic melanoma have substantially improved over the past decade with the advent of immune checkpoint inhibitors (ICIs), and BRAF/MEK inhibitors [[1], [2], [3], [4], [5], [6], [7]]. However, a considerable number of patients do not experience durable disease control, and predictive biomarkers that can be assessed prior to treatment initiation are currently lacking.
Liquid biopsy represents a promising non-invasive tool that may be used for monitoring treatment response, treatment-related adverse events and the onset of treatment resistance [8]. In this scenario, the circulating tumor DNA (ctDNA), representing fragments of DNA shed by tumor cells into the bloodstream, offers a non-invasive means for dynamic evaluation of patients with advanced melanoma under treatment [9]. The detection and quantification of ctDNA have been promoted by the development of highly sensitive techniques, such as digital droplet PCR (ddPCR). This technology enhances the accuracy of ctDNA measurement by partitioning samples into thousands of droplets, each serving as an individual PCR reaction, thereby increasing the assay's resolution [10]. Recently, several studies investigated the role of ctDNA as a surrogate marker of tumor burden and treatment response in advanced melanoma [8,9,11].
An analysis of baseline ctDNA demonstrated that patients with detectable levels of BRAF p.V600E had worse overall survival (OS) and progression free survival (PFS) in response to dabrafenib or trametinib treatment compared with patients with undetectable BRAF p.V600E ctDNA [12]. In a separate analysis, lower levels of BRAF or NRAS ctDNA at baseline were significantly associated with response to therapy regardless of treatment received [13]. In a case series of patients with BRAF p.V600–mutant metastatic melanoma, ctDNA levels showed a rapid decline following initiation of dabrafenib and trametinib, becoming undetectable in 60 % of patients within six weeks and correlating with significantly better PFS [14]. Conversely, increases in BRAF p.V600–mutant ctDNA levels during treatment were associated with clinical progression and preceded progressive disease in 44 % of cases [15]. These findings were also confirmed in a subsequent prospective phase 2 trial [16].
Emergent data suggest that the value of ctDNA analysis extends beyond patients with BRAF mutations treated with target therapy. In a recent longitudinal study, ctDNA profiles were shown to be sensitive and reliable markers for treatment outcomes and early detection of primary resistance in melanoma patients harboring BRAF or NRAS mutation treated with ICIs [[17], [18], [19]]. Similarly, ctDNA analysis at baseline and within 12-weeks of treatment accurately differentiated pseudo-progression, defined as radiologic progression not confirmed as progressive disease upon follow-up assessment, from true disease progression [20]. ctDNA-levels were also predictive of response to ICIs: patients with undetectable ctDNA had significantly longer PFS then those with detectable ctDNA [21,22].
However, release, circulation, and detection of ctDNA may be influenced by several factors, adding layers of complexity to the analysis. Moreover, the sensitivity and specificity of ctDNA assays need improvement for routine clinical application.
The endpoint of our prospective study was to provide a dynamic view of ctDNA monitoring in patients with advanced BRAF- or NRAS-mutant melanoma treated with ICIs using ddPCR. Therefore, we monitored the quality and quantity of ctDNA at each timepoint to identify possible correlations between ctDNA shedding and oncological outcomes.
2. Materials and methods
2.1. Study design and participants
The LIQUID-MEL study is a single-centre, prospective, explorative, pilot study conducted on patients with advanced BRAF p.V600 or NRAS-mutant melanoma consecutively undergoing systemic therapy with ICIs (ipilimumab plus nivolumab or nivolumab) at the Medical Oncology Unit of the University Hospital of Parma (Italy), between August 2019 and November 2022.
Eligibility criteria included a histologically confirmed diagnosis of metastatic cutaneous melanoma harbouring BRAF or NRAS mutation, as detected through routine mutational analysis of biopsy specimens (either primary tumor or metastatic lesion if available). Patients must have been received systemic therapy with ICIs (ipilimumab plus nivolumab or nivolumab) at any treatment line for metastatic disease. Patients enrolled at the initiation of first-line therapy were also monitored during subsequent potential second-line targeted therapy, according to clinical practice. Written informed consent was obtained from all patients prior to any procedures. Exclusion criteria included synchronous malignancies aside from melanoma, treatments other than those described above or lack of available medical records.
The study procedures followed were in accordance with the declaration of Helsinki and the study was approved by the local ethical committee (229/2019/TESS/AOUPR, 14/06/2019).
2.2. Study procedures and methods
Blood samples were collected from each patient at four timepoints for each treatment line: before the beginning of systemic treatment (T0), before the administration of the second cycle of treatment (T1, 3–4 weeks), at the time of first radiological reassessment (T2, 2–3 months), at the time of the first radiological progression of disease (TPD).
Baseline BRAF/NRAS mutations were assessed by Easy BRAF and Easy NRAS kit (Diatech Pharmacogenetics®, Italy) following the manufacturer's instructions on tissue biopsy by the Pathology Unit of the University Hospital of Parma.
The analysis of BRAF/NRAS on liquid biopsy was performed on cell-free DNA (cfDNA). Blood was collected in 6 ml EDTA tubes and centrifuged twice for 10 min at 2000×g within 1 h after blood drawing. Plasma samples were stored at −80 °C until analysis.
cfDNA was extracted from 4 ml of plasma kit using MagCore® Super Automated Nucleic Acid Extractor (Diatech Labline).
The analysis of BRAF/NRAS on cfDNA was performed by BRAF V600 screening, NRAS Q61 screening and NRAS G12/G13 screening ddPCR Mutation Assays (BioRad®, Hercules, CA, USA). All the procedures were executed following the specific manufacturer's instructions, at the Medical Oncology Unit of the University Hospital of Parma. QX200 Droplet Generator (Bio-Rad) was performed to generate droplets evaluate using the QX200 Droplet Reader (Bio-Rad) and data was analysed using QuantaSoft version 1.7.4. Results are presented as ctDNA fractional abundance (number of droplets positive for mutant-assay/total number of mutant- and wt-assay positive droplets). Each sample was run in triplicate and where one of these generated a total of ≤3 droplets positive for the mutation assay were defined as having ctDNA not detected.
Patients were classified as "shedders" if they exhibited detectable levels of ctDNA containing the driver mutation.
2.3. Statistical analysis
All data about the clinical history of patients, including demographic information and comorbidities, concomitant medications, melanoma diagnosis and staging, histological and molecular features, number and sites of metastasis, outcomes to anticancer treatment and advent events, clinical management, was collected.
Patients and disease characteristics were described using rates (percentages), median values and ranges. Clinical outcome was assessed in terms of objective response rate (ORR), PFS and OS. ORR was defined as the proportion of patients experiencing an objective response (either complete response or partial response) as best response to immunotherapy according to Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST 1.1) [23]. PFS was defined as the time from systemic therapy initiation to the first documented tumor progression or death. OS was defined as the time from immunotherapy initiation until death from any cause. Patients not progressed/not died at the data cut-off of November 2022 were considered as censored at the time of the last follow-up The Kaplan–Meier method was used to estimate PFS and OS, and the log-rank test (Mantel-Cox) was applied to assess whether there were statistically significant differences in PFS and OS across subgroups. Univariable and multivariable Cox proportional hazards regression models were used to analyse the PFS and OS data. The results were expressed as Hazard Ratio (HR), 95 % confidence intervals (95 %CI), and p values. The stepwise multivariable model was developed considering only those variables with a p value < 0.10 at the univariable analysis. The level of statistical significance was set to a value of 0.05. Logistic regression was used to assess the correlation between subgroups and the ORR.
The software JAMOVI version 2.3.21 (www.jamovi.org) was used to perform all the computational analyses and to draw the survival curves.
3. Results
3.1. Patients’ characteristics
Out of the 26 patients enrolled in the study, 3 (11.5 %) were excluded due to screening failure, as no BRAF or RAS mutations were detected. Therefore, 23 patients were included in the final analysis. Baseline clinicopathological characteristics according to ctDNA shedding status, are presented in Table 1. The cohort consisted of 14 males (60.9 %), with a mean age of 64.4 years (±10.4). BRAF mutations were present in 15 patients (65.2 %), while 8 patients (34.8 %) harbored NRAS mutations. Of the BRAF-mutant cases, 12 patients (80 %) carried the BRAF p.V600E mutation. 17 patients (73.9 %) had synchronous metastatic disease at time of diagnosis. Brain involvement was observed in 7 patients (30.4 %), and 13 patients (56.6 %) had three or more metastatic sites. 16 patients (69.6 %) had not received adjuvant therapy, and 8 patients (34.8 %) received ICIs as a second-line treatment. Disease progression as best response was noted in 13 patients (56.5 %). Additionally, only half of the BRAF p.V600E-mutant patients received targeted therapy after ICIs, Table 1.
Table 1.
Baseline characteristics of the overall population and according to shedding at baseline (bold indicates statistically significant values). CNS: central nervous system; ECOG PS: Eastern Cooperative Oncology Group Performance Status; ICI: immune checkpoint inhibitor; irAEs: immune-related adverse events; LDH: lactate dehydrogenase; SD: standard deviation.
| Variables | Overall N = 23 |
No shedding at baseline N = 18 (78.3 %) |
Shedding at baseline N = 5 (21.7 %) |
p-value |
|---|---|---|---|---|
| Sex, n (%) Male Female |
14 (60.9) 9 (39.1) |
10 (55.6) 8 (44.4) |
4 (80.0) 1 (20.0) |
0.322 |
| Age, mean (SD) | 64.4 (10.4) | 64.4 (11.0) | 64.7 (9.3) | 0.950 |
| Lymphadenectomy (%) No Yes |
11 (47.8) 12 (52.2) |
9 (50.0) 9 (50.0) |
2 (40.0) 3 (60.0) |
0.692 |
| Presence of metastases at diagnosis (%) No Yes |
17 (73.9) 6 (26.1) |
13 (72.2) 5 (27.8) |
4 (80.0) 1 (20.0) |
0.726 |
| Type of mutation (%) BRAF NRAS |
15 (65.2) 8 (34.8) |
11 (61.1) 7 (38.9) |
4 (80.0) 1 (20.0) |
0.433 |
| Number of metastatic sites, n (%) <3 ≥3 |
10 (43.4) 13 (56.6) |
9 (50.0) 9 (50.0) |
1 (20.0) 4 (80.0) |
0.231 |
| Bone metastases, n (%) No Yes |
18 (78.3) 5 (21.7) |
15 (83.3) 3 (16.7) |
3 (60.0) 2 (40.0) |
0.263 |
| CNS metastases, n (%) No Yes |
16 (69.6) 7 (30.4) |
14 (77.8) 4 (22.2) |
2 (40.0) 3 (60.0) |
0.104 |
| Liver metastases, n (%) No Yes |
16 (69.6) 7 (30.4) |
13 (72.2) 5 (27.8) |
3 (60.0) 2 (40.0) |
0.599 |
| ECOG PS, n (%) 0 1 |
16 (69.6 %) 7 (30.4 %) |
14 (77.8) 4 (22.2) |
2 (40.0) 3 (60.0) |
0.104 |
| Previous adjuvant treatment, n (%) No Yes |
16 (69.6) 7 (30.4) |
12 (66.7) 6 (33.3) |
4 (80.0) 1 (20.0) |
0.567 |
| ICI treatment line, n (%) First-line Second-line |
15 (65.2) 8 (34.8) |
14 (77.8) 4 (22.2) |
1 (20.0) 4 (80.0) |
0.016 |
| Type of ICI, n (%) Ipilimumab plus nivolumab Nivolumab |
12 (52.2) 11 (47.8) |
9 (50.0) 9 (50.0) |
3 (60.0) 2 (40.0) |
0.692 |
| Best response, n (%) Complete response Partial response Stable disease Progressive disease |
2 (8.7) 7 (30.4) 1 (4.3) 13 (56.5) |
2 (11.1) 7 (38.9) 1 (5.6) 8 (44.4) |
0 (0.0) 0 (0.0) 0 (0.0) 5 (100.0) |
0.178 |
| IrAEs G3-G4, n (%) No Yes |
19 (82.6) 4 (17.4) |
15 (83.3) 3 (16.7) |
4 (80.0) 1 (20.0) |
0.862 |
| Steroid during ICI (≥10 mg prednisone), n (%) No Yes |
12 (52.2) 11 (47.8) |
9 (50.0) 9 (50.0) |
3 (60.0) 2 (40.0) |
0.692 |
| Target therapy after ICI, n (%) No Yes |
17 (73.9) 6 (26.1) |
13 (72.2) 5 (27.8) |
4 (80.0) 1 (20.0) |
0.726 |
| LDH, mean (SD) | 349.9 (433.8) | 251.3 (88.1) | 704.6 (895.0) | 0.035 |
At baseline, ctDNA shedding was detectable in 5 patients (21.7 %). No significant differences were observed between the shedding and non-shedding groups across most variables, except for ICI treatment line and LDH. Patients in the non-shedding group were more likely to receive ICIs as first-line treatment and had lower baseline LDH levels. Supplementary Table 1 summarized blood sample collection timepoints. Among the 5 shedder patients at T0, only 1 achieved clearance at T1. The mean variant allelic frequency (VAF) at T0 was 10 % (SD 13.5). At T1, no shedding was detected, while at T2 only 2 patients were shedders, with VAFs of 2.8 % and 1.9 %. At TPD, 8 patients were shedders, with a mean VAF of 18 % (SD 15.8).
Following disease progression, only 4 patients started second-line treatment with target therapy. For these patients, shedding at TPD was considered as T0 for the second-line (II) treatment (mean VAF = 13.2 %, SD 15.1). Interestingly, 2 patients had clearance at T1 (II), while another 2 patients achieved clearance at T2 (II). Finally, 2 patients had shedding at TPD (II), with a VAF of 3.2 % and 1.3 %, Supplementary Table 1.
3.2. Survival analysis in overall population and according to shedding status
The median follow-up time was 24.7 months. In the overall population, median PFS (mPFS) was 2.24 months (95 % CI: 0.89–35.2), Supplementary Fig. 1. Kaplan-Meier analysis showed a statistically significant longer mPFS in non-shedder patients compared to shedder patients at baseline (3.88 months [95 % CI: 1.18–not calculated] vs. 0.69 months [95 % CI: 0.66–not calculated], p=0.012), Fig. 1.
Fig. 1.
Representative Kaplan-Meier survival curve illustrating the impact of shedding on progression-free survival.
At the univariable analysis for PFS, the following factors were significantly associated with an increased risk of progression or death: three or more metastatic sites, CNS metastases, an ECOG PS of 1 compared to ECOG PS 0, receiving ICI as a second-line treatment, and baseline ctDNA shedding. At the multivariable analysis, three factors remained statistically significant: CNS involvement (HR 19.60 [95 % CI: 1.74–221.03], p=0.016), second-line ICI treatment (HR 11.64 [95 % CI: 1.73–78.48], p=0.012), and nivolumab monotherapy (HR 0.24 [95 % CI: 0.06–0.86], p=0.028). Notably, baseline ctDNA shedding did not maintain its prognostic impact in the multivariable analysis, Table 2.
Table 2.
Univariable and multivariable analyses for progression-free survival (bold indicates statistically significant values). CNS: central nervous system; ECOG PS: Eastern Cooperative Oncology Group Performance Status; ICI: immune checkpoint inhibitor; irAEs: immune-related adverse events.
| Variables | Patients (%) | Univariable |
p-value | Multivariable |
p-value | ||
|---|---|---|---|---|---|---|---|
| HR | 95 % CI | HR | 95 % CI | ||||
| Sex, n (%) Male Female |
14 (60.9) 9 (39.1) |
1 0.79 |
[0.30–2.08] | 0.635 | |||
| Lymphadenectomy (%) No Yes |
11 (47.8) 12 (52.2) |
1 0.65 |
[0.25–1.72] | 0.385 | |||
| Presence of metastases at diagnosis (%) No Yes |
17 (73.9) 6 (26.1) |
1 0.92 |
[0.30–2.81] | 0.881 | |||
| Type of mutation (%) BRAF NRAS |
15 (65.2) 8 (34.8) |
1 0.55 |
[0.19–1.57] | 0.264 | |||
| Number of metastatic sites, n (%) <3 ≥3 |
10 (43.4) 13 (56.6) |
1 2.54 |
[0.93–6.96] | 0.070 | 0.56 | [0.12–2.57] | 0.459 |
| Bone metastases, n (%) No Yes |
18 (78.3) 5 (21.7) |
1 1.07 |
[0.35–3.30] | 0.908 | |||
| CNS metastases, n (%) No Yes |
16 (69.6) 7 (30.4) |
1 8.81 |
[2.18–35.52] | 0.002 | 19.60 | [1.74–221.03] | 0.016 |
| Liver metastases, n (%) No Yes |
16 (69.6) 7 (30.4) |
1 1.43 |
[0.49–4.13] | 0.512 | |||
| ECOG PS, n (%) 0 1 |
16 (69.6 %) 7 (30.4 %) |
1 4.39 |
[1.19–16.17] | 0.026 | 1 0.53 |
[0.06–4.37] | 0.553 |
| Previous adjuvant treatment, n (%) No Yes |
16 (69.6) 7 (30.4) |
1 0.89 |
[0.33–2.37] | 0.811 | |||
| ICI treatment line, n (%) First-line Second-line |
15 (65.2) 8 (34.8) |
1 5.13 |
[1.81–14.54] | 0.002 | 11.64 | [1.73–78.48] | 0.012 |
| Type of ICI, n (%) Ipilimumab plus nivolumab Nivolumab |
12 (52.2) 11 (47.8) |
1 0.37 |
[0.14–1.00] | 0.050 | 0.24 | [0.06–0.86] | 0.028 |
| IrAEs G3-G4, n (%) No Yes |
19 (82.6) 4 (17.4) |
1 0.62 |
[0.18–2.19] | 0.458 | |||
| Steroid during ICI (≥10 mg prednisone), n (%) No Yes |
12 (52.2) 11 (47.8) |
1 1.52 |
[0.60–3.87] | 0.381 | |||
| Target therapy after ICI, n (%) No Yes |
17 (73.9) 6 (26.1) |
1 1.02 |
[0.37–2.76] | 0.975 | |||
| Shedding at baseline, n (%) No Yes |
18 (78.3) 5 (21.7) |
1 4.07 |
[1.26–13.17] | 0.019 | 0.66 | [0.13–3.38] | 0.614 |
In the overall population, median OS (mOS) was 4.84 months (95 % CI: 2.24–not calculated), Supplementary Fig. 2. Kaplan-Meier analysis did not show a statistically significant difference in mOS between non-shedder and shedder patients at baseline, although a strong numerical trend favoring non-shedder patients was observed (12.66 months [95 % CI: 1.51–not calculated] vs. 2.53 months [95 % CI: 2.27–not calculated], p=0.287), Fig. 2.
Fig. 2.
Representative Kaplan-Meier survival curve illustrating the impact of shedding on overall survival.
In the univariable analysis for OS, the following factors were associated with a significant increase in the risk of death: three or more metastatic sites, CNS involvement, ECOG PS of 1 compared to ECOG PS 0, and second-line ICI treatment. However, in the multivariable analysis, only ECOG PS retained significance (HR 5.65 [95 % CI: 1.20–26.54], p=0.028), Table 3.
Table 3.
Univariable and multivariable analyses for overall survival (bold indicates statistically significant values). CNS: central nervous system; ECOG PS: Eastern Cooperative Oncology Group Performance Status; ICI: immune checkpoint inhibitor; irAEs: immune-related adverse events.
| Variables | Patients (%) | Univariable |
p-value | Multivariable |
p-value | ||
|---|---|---|---|---|---|---|---|
| HR | 95 % CI | HR | 95 % CI | ||||
| Sex, n (%) Male Female |
14 (60.9) 9 (39.1) |
1 0.70 |
[0.23–2.08] | 0.516 | |||
| Lymphadenectomy (%) No Yes |
11 (47.8) 12 (52.2) |
1 0.41 |
[0.14–1.24] | 0.116 | |||
| Presence of metastases at diagnosis (%) No Yes |
17 (73.9) 6 (26.1) |
1 1.16 |
[0.37–3.65] | 0.804 | |||
| Type of mutation (%) BRAF NRAS |
15 (65.2) 8 (34.8) |
1 0.70 |
[0.22–2.22] | 0.541 | |||
| Number of metastatic sites, n (%) <3 ≥3 |
10 (43.4) 13 (56.6) |
1 3.26 |
[1.03–10.35] | 0.045 | 2.22 | [0.59–8.38] | 0.238 |
| Bone metastases, n (%) No Yes |
18 (78.3) 5 (21.7) |
1 0.97 |
[0.27–3.51] | 0.967 | |||
| CNS metastases, n (%) No Yes |
16 (69.6) 7 (30.4) |
1 5.30 |
[1.64–17.12] | 0.002 | 1.50 | [0.36–6.19] | 0.577 |
| Liver metastases, n (%) No Yes |
16 (69.6) 7 (30.4) |
1 2.21 |
[0.77–6.34] | 0.138 | |||
| ECOG PS, n (%) 0 1 |
16 (69.6 %) 7 (30.4 %) |
1 7.02 |
[1.99–24.70] | 0.002 | 5.65 | [1.20–26.54] | 0.028 |
| Previous adjuvant treatment, n (%) No Yes |
16 (69.6) 7 (30.4) |
1 0.57 |
[0.16–2.04] | 0.385 | |||
| ICI treatment line, n (%) First-line Second-line |
15 (65.2) 8 (34.8) |
1 3.01 |
[1.08–8.36] | 0.035 | 2.52 | [0.78–8.19] | 0.124 |
| Type of ICI, n (%) Ipilimumab plus nivolumab Nivolumab |
12 (52.2) 11 (47.8) |
1 0.57 |
[0.20–1.67] | 0.307 | |||
| IrAEs G3-G4, n (%) No Yes |
19 (82.6) 4 (17.4) |
1 0.29 |
[0.04–2.19] | 0.228 | |||
| Steroid during ICI (≥10 mg prednisone), n (%) No Yes |
12 (52.2) 11 (47.8) |
1 0.86 |
[0.30–2.50] | 0.789 | |||
| Target therapy after ICI, n (%) No Yes |
17 (73.9) 6 (26.1) |
1 0.69 |
[0.22–2.19] | 0.531 | |||
| Shedding at baseline, n (%) No Yes |
18 (78.3) 5 (21.7) |
1 1.79 |
[0.60–5.33] | 0.294 | |||
Regarding the ORR, logistic regression analysis did not identify any variables significantly associated with treatment response.
3.3. Longitudinal single patients’ analysis
Table 4and Fig. 3, report a series of patients with BRAFp.V600 mutations and different status of ctDNA shedding at baseline. Particularly, P-08 and P-13 were non-shedder at the beginning of first-line ICI therapy. ctDNA became detectable at T2 (first imaging evaluation) and raised at the time of radiological progression. At the first-time point after the initiation of second-line target therapy (T1 II), ctDNA reverted to an undetectable state. Interestingly, P-08 had a new shedding flare at the time of radiological progression to the target therapy (TPD II). P-07 was the only shedder patient to achieve complete clearance after ICI therapy at T1. P-07 and P-23 had a flare of ctDNA shedding at T2, the first radiological evaluation, which matched with radiological disease progression. Both patients started second-line target-therapy and the subsequent timepoint (T1 II) showed an important decrease of shedding, together with a significant clinical response. Finally, at T2 (II), these patients achieved complete clearance. Interestingly, P-23 experienced another ctDNA flare at the time of radiological progression to second-line treatment (TPD II). P-10, P-12 and P-19, who were shedders at baseline, experienced a significant increase in the ctDNA between the start of ICI and the subsequent sample collected (T1), which also coincided with clinical progression of disease.
Table 4.
Dynamic changes of ctDNA for representative patients. T0, start of systemic treatment; T1, before second cycle (3–4 weeks); T2, first radiological reassessment (2–3 months); TPD, radiological progression of disease (TPD in a cell denotes progression timepoint); D, death before or near the collection timepoint; (II), second-line treatment.
| Mutation | T0 | T1 | T2 | TPD | T0 (II) | T1 (II) | T2 (II) | TPD (II) | |
|---|---|---|---|---|---|---|---|---|---|
| P-07 | BRAF p.V600E | 0.11 % | WT | TPD | 5.50 % | 5.50 % | 0.17 % | WT | - |
| P-08 | BRAF p.V600K | WT | WT | 2.80 % | 5.50 % | 5.50 % | WT | TPD (II) | 3.20 % |
| P-10 | BRAF p.V600E | 1.83 % | TPD | TPD | 35.00 % | D | |||
| P-12 | BRAF p.V600E | 30.00 % | TPD | TPD | 37.00 % | D | |||
| P-13 | BRAF p.V600K | WT | WT | 1.90 % | 6.00 % | 6.00 % | WT | - | - |
| P-19 | BRAF p.V600K | 0.12 % | TPD | TPD | 19.00 % | D | |||
| P-23 | BRAF p.V600E | WT | WT | TPD | 35.80 % | 35.80 % | 0.34 % | WT | 1.30 % |
Fig. 3.
Dynamic changes of ctDNA for representative patients. T0, start of systemic treatment; T1, before second cycle (3–4 weeks); T2, first radiological reassessment (2–3 months); TPD, radiological progression of disease (TPD in a cell denotes progression timepoint); D, death before or near the collection timepoint; (II), second-line treatment.
4. Discussion
Ever since the spread of ICIs, the prognosis of patients with both early-stage and advanced melanoma has significantly improved [24,25]. Unfortunately, almost a quarter of patients do not show any benefit from first-line therapy with ICIs [26], therefore making imperative the identification of prognostic and predictive biomarkers to monitor clinical course and early identify disease progression. Liquid biopsy through ctDNA monitoring is a promising and not invasive tool which could identify driver mutations, capture cancer dynamism by repeating during the course of treatment, and early detect relapse or progression [27,28]. Indeed, ctDNA levels have been related to tumor burden and response to therapy, in various malignancies, including melanoma [[29], [30], [31]]. Known pathogenetic genes suitable for ctDNA analysis include BRAF, GNAQ, JAK2, KRAS, NRAS, MAP2K1, NF1, c-KIT and STAT1 [32]. Epigenetic changes and hypermethylation of promoter regions (such as RARB2, TFPI2, MGMT) are also established biomarkers, the latter helpful for an early diagnosis of melanoma [[33], [34], [35]]. A higher concentration of ctDNA at the time of melanoma diagnosis was associated to high tumor load and worse OS, while an increase in ctDNA shedding between baseline and following evaluations, negatively influenced survival [30,36,37].
In our prospective, explorative, pilot study, we aimed to dynamically monitor BRAF- or NRAS-mutant ctDNA of patients with advanced melanoma during systemic therapy with ICIs. Although shedding at baseline was only detectable in 5 patients (21.7 %), our results suggest that ctDNA shedding was associated with shorter PFS with a statistically significant difference between shedder and non-shedder patients (mPFS 3.88 months vs. 0.69 months). However, ctDNA shedding resulted not statistically significant in the multivariable analysis. No significant difference was observed in the OS analysis between the shedding and non-shedding groups, though a numerical trend favoring non-shedders was noted.
Our percentage of patients with detectable ctDNA at baseline is in line with the detection rate reported in literature, which ranges between 11 % up to 80 % [38]. This variability depends on the detection technology used, tumor burden, and the sites of metastatic disease, including brain involvement, which can ultimately reduce the fraction of ctDNA detectable in the bloodstream [29].
Our findings on the correlation between shedding and oncological outcomes are consistent with previous reports. A recent meta-analysis by Feng et al. including 617 patients with melanoma, confirmed the correlation between detectable ctDNA at diagnosis and poor survival (HR 2.91, 95 % CI: 2.22–3.82; p < 0.001) [39].
In our analysis, ctDNA shedding did not result as an independent prognostic factor in the multivariable analysis. Overall, multivariable analysis for PFS and OS reinforced the role of clinical factors, such as CNS involvement, ECOG PS and burden of disease as main prognostic factors. These results suggest that the prognostic value of ctDNA shedding may be overshadowed by established clinical variables, considering the limits of the small sample size.
Notably, our longitudinal analysis revealed intriguing patterns of ctDNA shedding in individual patients, especially those treated with second-line target therapy. Indeed, changes in ctDNA shedding were closely associated with treatment response and disease progression, highlighting the potential role of ctDNA as a real-time and dynamic marker of tumor behaviour. Our findings align with prior studies showing that changes in BRAF ctDNA levels during treatment with BRAF/MEK inhibitors can reflect radiologic response, with a decreasing related to response and a rising in the event of progression [15,37]. The level of mutant BRAF in ctDNA also correlated with the duration of response to target therapy, as well as with OS and PFS [40,41]. In a retrospective analysis on patients holding BRAF/MEK inhibitors due to cumulative toxicity, patients with complete response and negative ctDNA at time of discontinuation showed no progression of disease [42]. These findings were confirmed in a recent prospective study on 17 patients with stage IV melanoma treated with BRAF/MEK inhibitors [43]. The amount of ctDNA, both at baseline and during response, correlated with the type and duration of response, validating its prognostic role [43]. Additionally, the authors reported a combined approach of NGS and ddPCR assays. NGS panel showed a significant correlation with ddPCR and allowed the parallel monitoring of target genes together with the assessment of the BRAF copy number variation, increasing the sensitivity of the detection [43]. While c-KIT mutations are not directly addressed in our study, they are detected in specific melanoma subgroups, particularly in mucosal (MM), acral (AM) and chronically sun-damaged cutaneous subtypes [44]. MM and AM have a less favorable prognosis compared with cutaneous melanoma, although c-KIT mutations could confer sensitivity to TKIs like imatinib. However, response depends on mutation type and anatomical site [45]. Liquid biopsy enables real-time monitoring of c-KIT mutational status, capturing clonal evolution and resistance mechanisms during therapy. Therefore, tracking ctDNA in these patients could inform therapeutic decisions, such as switching TKIs or combining with ICIs, based on emerging resistance [45]. In this scenario, ongoing studies are investigating the role of TKIs combined with ICIs in c-KIT mutated melanoma [44].
Regarding ctDNA role in patients receiving ICIs, multiple data confirmed its feasibility as a marker of response, exceeding LDH and S100 dynamics [46]. Prospective evidence in patients with stage III and IV melanoma demonstrated that ctDNA levels correlated better with tumor burden and metabolic activity, measured as metabolic tumor volume (MTV) on PET/CT, than LDH or S100 [47]. Furthermore, early changes in ctDNA levels after treatment initiation accurately predicted RECIST response and were associated with improved OS and PFS, showing higher sensitivity and both negative and positive predictive values compared to LDH and S100 [48].In our longitudinal single patient's analysis, only 1 patient achieved complete clearance during ICI therapy. Intriguingly, ctDNA flares during treatment with ICIs corresponded to clinical and radiological progression. ctDNA detection rates were in line with the burden of disease, with lower detection at early timepoints (T0 = 21.7 %, T1 = 22.2 %) and higher detection at progression, when shedding is more likely (TPD = 44.4 %, TPD (II) = 50 %), Supplementary Table 1. According to our findings, several studies described the ability of ctDNA shedding to reflect the behaviour of the disease in patients with advanced melanoma receiving ICIs [18,30,49]. In these patients, a decreasing in ctDNA during treatment was associated with better response and survival [17], and an enduring decrease was also related to prolonged radiological responses [50]. Conversely, an increase in ctDNA during ICIs was able to predict disease progression about 3.5 months earlier than radiologic imaging [51]. These findings were confirmed in a prospective trial on 48 patients, where ctDNA reduction during ICIs were associated with favorable survival outcomes [9]. Interestingly, for each unit rise in ctDNA shedding from baseline, an increased risk of PD of 24 % was described [9]. Further confirmatory results come from another recent prospective study that enrolled 104 patients receiving palliative and adjuvant ICIs [47]. A decrease of variant allele fractions correlated with metabolic response on PET and improved survival outcomes in the palliative cohort. Additionally, ctDNA monitoring detected early recurrence in the adjuvant cohort, therefore paving the way for its use in this setting [47]. Similarly to what described with BRAF/MEK inhibitors, a lower DNA level at time of ICIs holding after durable response, was related to longer treatment-free interval [52]. Finally, longitudinal ctDNA monitoring was able to distinguish between pseudo-progression and radiological true PD to ICIs, showing a sensitivity and specificity for predicting pseudo-progression of 90 % (95 %CI, 68 %–99 %) and 100 % (95 %CI, 60 %–100 %), respectively [20,53].
Our study has major limitations. First, the small sample size and the limited percentage of patients with ctDNA shedding affected the statistical power of the analysis, as well as reliability, causality and generalizability of results. This represents the main limitation of our study, together with the single-centre design, which limits the external validity of our results. Additionally, the study was conducted using ddPCR and not NGS, therefore missing information on tumor heterogeneity and potential mechanisms of resistance. Indeed, tumor heterogeneity, differences in metastatic sites, use of different ICIs, and variations in ctDNA extraction protocols could influence ctDNA levels and their interpretation.
Several pre-analytical variables may have influenced ctDNA detection. Temporal variability of liquid biopsy collection, delayed plasma separation and centrifugation, different centrifugation speed and low plasma volumes, as well as limitations inherent to the ddPCR assay, particularly its targeted nature and threshold for positivity, may have impaired sensitivity and detection rates. These observations highlight the need for standardized pre-analytical protocols, including collection tubes, immediate processing, adequate plasma volumes, and centrifugation steps. At the analytical level, consensus parameters on assay sensitivity, detection limits and sample quality should be validated to support broader implementation of liquid biopsy. Eventually, as sequencing costs decrease, tumor-uninformed NGS-based approaches hold higher sensitivity and broader mutation panels. Integration of ctDNA into clinical practice requires not only analytical validation, but also robust demonstration of clinical utility. Randomized prospective trials are essential to determine whether clinical decision making based on ctDNA could improve treatment outcomes compared to standard monitoring and guide personalized treatment strategies [54].
5. Conclusion
Despite the above limitations, our study reinforced the potential utility of ctDNA as a dynamic biomarker for disease monitoring in real-world patients with advanced melanoma, and the use of ddPCR for highly sensitive analysis. These data need to be further validated in larger, multicentre prospective trials.
Consent to participate
The obtainment of informed consent for live patients was mandatory. Informed consent was obtained from all individual participants included in the study.
Compliance with ethical standards/ethics approval
The study was conducted following the approval by the Local ethics committee (229/2019/TESS/AOUPR, 14/06/2019)). The study was performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.
Author contributions
All Authors contributed to the conception and design of the study, collecting and curating data, drafting, editing and revising the manuscript critically. Michele Maffezzoli has also given substantial contribution to data analysis and methodology. Michele Maffezzoli, Roberta Minari, Fabiana Perrone and Giulia Mazzaschi has also contributed to supervision, validation and visualization of the manuscript. All authors read and approved the final version of the manuscript. All authors have sufficiently participated to the study and agreed to be accountable for all aspects of the work.
Data availability
All data generated or analysed during this study are included in this published article and its supplementary information files.
Funding
The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.
Declaration of conflict interests
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
None. The authors report no involvement in the research by the sponsor that could have influenced the outcome of this work.
Footnotes
Supplementary data to this article can be found online at https://doi.org/10.1016/j.jlb.2025.100295.
Appendix A. Supplementary data
The following is the Supplementary data to this article:
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Data Availability Statement
All data generated or analysed during this study are included in this published article and its supplementary information files.