Abstract
Background
CDK4/6 inhibitors (CDK4/6i) in combination with endocrine therapy (ET) represent the standard of care for hormone receptor–positive, HER2-negative (HR+/HER2–) metastatic breast cancer (MBC) patients. However, no head-to-head randomized trials have directly compared palbociclib, ribociclib, and abemaciclib. Moreover, predictive biomarkers of resistance to CDK4/6i remain largely undefined. This study aimed to evaluate circulating tumor DNA (ctDNA)-based epigenetic and fragmentomic biomarkers as potential predictors of response and resistance in patients receiving CDK4/6i.
Methods
We conducted a biomarker-driven analysis within the prospective, multicenter MAGNETIC.1 study, enrolling 149 patients with HR+/HER2– MBC treated with first-line endocrine therapy and a CDK4/6 inhibitor. Plasma samples were collected at baseline and during treatment (3 and 6 months). Droplet digital PCR was used to assess ESR1 promoter methylation and ACTB fragmentomic profiles. Progression-free survival (PFS) and overall survival (OS) were evaluated, and molecular dynamics were compared between treatment groups.
Results
After a median follow-up of 34.8 months, no statistically significant differences in PFS or OS were observed between ribociclib and palbociclib treated patients, although ribociclib was associated with numerically longer PFS and higher survival rates. At the molecular level, palbociclib treatment was characterized by transient increases in ESR1 promoter methylation at the first evaluation and a rebound in ACTBshort fragment levels at six months relative to baseline. These dynamic patterns were not observed among patients receiving ribociclib.
Conclusions
ctDNA-based methylation and fragmentomic profiling revealed exploratory, treatment specific molecular dynamics, highlighting biological differences between CDK4/6 inhibitors. These findings support the feasibility of liquid biopsy–based biomarker studies in this setting, although their potential clinical relevance remains preliminary and requires validation in larger cohorts with earlier and more granular on-treatment timepoints.
Keywords: Metastatic breast cancer, CDK4/6 inhibitors, Liquid biopsy, Fragmentomics, Methylomics, Circulating tumor DNA
Highlights
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Prospective real-world study comparing palbociclib and ribociclib in 149 patients with HR+/HER2– metastatic breast cancer.
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No statistically significant differences in progression-free or overall survival between treatment groups.
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ESR1 promoter methylation exhibits early, treatment-specific dynamic changes.
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ctDNA fragmentomic profiling reveals distinct resistance-associated patterns.
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Liquid biopsy–based biomarkers may support personalized selection of CDK4/6 inhibitors.
1. Introduction
Hormone receptor positive (HR+)/Human epidermal growth factor receptor 2 negative (HER2-) subtype accounts for approximately 68 % of metastatic breast cancer (MBC) cases [1,2].
Over the last decade, cyclin-dependent kinase 4 and 6 inhibitors (CDK4/6i) have revolutionized the treatment landscape in this setting, both in endocrine sensitive and endocrine resistant tumors [1,2]. Palbociclib, ribociclib and abemaciclib, when combined with aromatase inhibitors (AI), fulvestrant or tamoxifen, consistently extended progression-free survival (PFS) compared to endocrine therapy (ET) alone and are now guideline-endorsed as first-line therapy for HR+/HER2- MBC [[3], [4], [5], [6], [7], [8], [9], [10], [11], [12]].
Despite comparable outcomes in terms of PFS, however, notable discrepancies exist regarding overall survival (OS) [7,10], and nearly all patients experience primary or secondary resistance, with a median PFS (mPFS) in the first-line setting ranging from 23 to 28 months [13]. Resistance mechanisms include activation of oncogenic signaling pathways and diminished reliance on estrogen receptor (ER) signaling through genetic and epigenetic alterations. In particular, hypermethylation of ESR1 regulatory regions, including promoters and enhancers, has been linked to transcriptional silencing of ESR1 silencing and consequent loss of ER expression [[14], [15], [16]], although promoter hypermethylation represents only one of several mechanisms capable of reducing ESR1 transcription, as loss of ER expression may also arise through genetic alterations, chromatin remodeling, inflammatory signaling, or lineage plasticity programs.
Thus, reliable predictive biomarkers of primary and acquired resistance are needed to optimize therapeutic strategies in the CDK4/6i era. Liquid biopsy, particularly through circulating tumor DNA (ctDNA) analysis, has emerged as a promising non-invasive tool, providing valuable insights into tumor biology and treatment-induced evolutionary dynamics [17]. Yet, in current clinical practice, the choice of first-line CDK4/6i is still largely guided by toxicity profiles and comorbidities, rather than tumor-specific molecular features. Moreover, no prospective head-to-head studies have directly compared the molecular efficacy or ctDNA dynamics of palbociclib, ribociclib, and abemaciclib [18,19]. To address this knowledge gap, we conducted an exploratory analysis within a prospective real-world cohort by integrating novel ctDNA biomarkers, including ESR1 promoter methylation and ACTB fragment-based profiling. Our aim was to capture drug-specific molecular responses and identify potential predictors of benefit or resistance, ultimately supporting a more personalized approach to first-line treatment of HR+/HER2- MBC patients.
2. Methods
2.1. Study design and statistical analysis
Between January 2018 and January 2023, a total of 149 women with HR+/HER2- MBC were prospectively enrolled in the multicenter, pragmatic, observational MAGNETIC.1 study (NCT05814224). All patients received first-line endocrine therapy with either fulvestrant or aromatase inhibitors, with or without a CDK4/6i, according to the investigator's choice. Key exclusion criteria included prior ET for MBC and any history of secondary malignancy within the preceding three years. Patients were allowed to have received neoadjuvant or adjuvant chemotherapy and/or ET.
Peripheral blood samples were collected at three predefined timepoints: at baseline (T0), prior to treatment initiation; after 3 months (T3); and after 6 months (T6), coinciding with routine radiological assessment by computed tomography (CT) scan. The study was approved by the institutional ethics committee (protocol CRO-2018-56), and all patients provided written informed consent.
Descriptive analyses were used for clinical and pathological variables. Categorical variables were presented as frequency distributions, while continuous variables were summarized using median and interquartile ranges (IQRs). Comparisons of continuous biomarker values between groups were performed using the Mann–Whitney test for independent samples. Multiple testing across timepoint comparisons was addressed using Bonferroni-adjusted p-values.
Progression-free survival was defined as the time from study enrollment to disease progression (according to RECIST v1.1) or death from any cause, or the date of the last follow-up. Overall survival was defined as the time from enrollment to death from any cause.
Survival analysis was performed using the log-rank test. Statistical analyses were conducted using StataCorp 2019 Stata Statistical Software: Release 16.1 (College Station, TX). Two-sided P-values <0.05 were considered statistically significant.
2.1.1. Droplet digital PCR for ACTB fragments and ESR1 promoter epigenetic status
Blood collected in Cell-Free DNA BCT tubes (Streck) was centrifuged at 1900×g for 15 min. The plasma was then collected, transferred into new tubes, and centrifuged again at 1900×g for 10 min. For storage, plasma was aliquoted into 2 mL cryovials. ctDNA was isolated from 4.8 mL of plasma using the QIAsymphony SP instrument (Qiagen) and eluted in 60 μL of elution buffer (Qiagen). ctDNA concentration was quantified using the Qubit 1X dsDNA HS Assay Kit (Qiagen). No further freezing and thawing cycles were performed between cfDNA extraction and analysis using ddPCR.
To assess cytolysis-derived contamination and quantify ctDNA, the cell-free DNA (cfDNA) sample was analyzed using droplet digital PCR (ddPCR) by the detection of short (136 bp) (ctDNA-associated), medium (420 bp), and long (2000 bp) (genomic DNA-associated) ACTB fragments as previously described [20]. Short actin-conjugated 5′ 6-FAM and medium actin-conjugated 5′ HEX (Bio-Rad) assays were used. The underlying principle of this approach is that plasma DNA stems from distinct biological processes: short fragments are typically associated with ctDNA and reflect fragmentation patterns characteristic of tumor cell release, whereas medium and long fragments are generally attributed to genomic DNA (gDNA) released from non-tumoral cells, such as leukocytes, through cellular lysis [21,22].
The cfDNA samples were also analyzed using ddPCR to determine the methylation status of the ESR1 promoter, as previously described [16]. Briefly, the Methprimer 2.0 online platform was used to evaluate probes designed as follows: one labeled in 6-FAM and specific for methylated DNA and the other labeled in HEX specific for unmethylated DNA. The EZ DNA Methylation-GoldTM kit (Zymo Research) was used, prior to ddPCR, for bisulfite conversion of 10 ng of ctDNA, following the manufacturer's standard protocol. Extended information on ddPCR assay design (primers, probes, off-target evaluation) and bisulfite conversion quality assessment is provided in the Supplementary Methods.
All analyses performed by ddPCR were performed in duplicate and then merged for further analysis. Drops were generated with the QX200 AutoDG™ droplet generator (Bio-Rad) and, after PCR amplification, were read with the QX200™ droplet reader (Bio-Rad). The data were analyzed with QuantaSoft™ 1.7.4 software (Bio-Rad). The presence of ESR1 alterations was assessed by Next-Generation Sequencing (NGS), with full methodological details provided in the respective section of Supplementary.
3. Results
Between January 2018 and January 2023, 149 patients with HR+/HER2– metastatic breast cancer treated with first-line endocrine therapy were prospectively enrolled. The majority had progesterone receptor–positive disease (77.5 %), and 62 % presented with de novo metastatic breast cancer. Bone was the most frequent site of metastasis (73 %), followed by lymph nodes (54 %), liver (30 %), and lung (24 %). The endocrine therapy backbone primarily consisted of aromatase inhibitors (72 %), with fulvestrant used in 28 % of cases. Overall, 95 % of patients received a CDK4/6i, with palbociclib (PAL) being the most commonly administered agent (43 %), followed by ribociclib (RIB) (41 %) and abemaciclib (16 %). At baseline, 8 patients (7 %) harbored an ESR1 mutation detectable in plasma. The low prevalence of ESR1 mutated cases did not allow appropriate statistical testing across treatment or clinical subgroups. Baseline characteristics were homogeneously distributed between groups (Table 1). Total plasma cfDNA concentrations across treatment groups at baseline are reported in Table 1.
Table 1.
Clinical characteristics of the study population overall and stratified by CDK subtype.
| N |
% |
Palbociclib (62, 43 %) |
Ribociclib (60, 41 %) |
Abemaciclib (23, 16 %) |
|
|---|---|---|---|---|---|
| Histotype | |||||
| IDC | 98 | 80.33 | 43 (42.60 %) | 40 (40.20 %) | 15 (15.30 %) |
| ILC | 17 | 13.93 | 8 (47.06 %) | 7 (41.18 %) | 2 (11.76 %) |
| MXD | 7 | 5.74 | 2 (28.57 %) |
3 (42.86 %) |
2 (28.57 %) |
| PR | |||||
| Positive | 107 | 77.54 | 48 (44.86 %) | 42 (39.25 %) | 17 (15.89 %) |
| Negative | 31 | 22.46 | 10 (32.26 %) | 17 (54.84 %) | 4 (12.90 %) |
| Liver | |||||
| Yes | 42 | 29.58 | 21 (50.00 %) | 11 (26.19 %) | 10 (23.81 %) |
| No | 100 | 70.42 | 39 (39 %) | 48 (48 %) | 13 (13 %) |
| Lung | |||||
| Yes | 34 | 23.94 | 10 (29.41 %) | 14 (41.18 %) | 10 (29.41 %) |
| No | 108 | 76.06 | 50 (46.30 %) | 45 (41.67 %) | 13 (12.04 %) |
| Bone | |||||
| Yes | 104 | 73.28 | 44 (42.31 %) | 42 (40.38 %) | 18 (17.31 %) |
| No | 38 | 26.76 | 16 (42.11 %) | 17 (44.74 %) | 5 (13.16 %) |
| Nodes | |||||
| Yes | 77 | 54.23 | 38 (49.35 %) | 28 (36.36 %) | 11 (14.29 %) |
| No | 65 | 45.77 | 22 (33.85 %) | 31 (47.69 %) | 12 (18.46 %) |
| Serosa | |||||
| Yes | 30 | 24 | 16 (53.33 %) | 10 (33.33 %) | 4 (13.33 %) |
| No | 95 | 76 | 44 (46.32 %) | 40 (42.11 %) | 11 (11.58 %) |
| Endocrine therapy | |||||
| AI | 104 | 72.22 | 45 (43.27 %) | 46 (44.23 %) | 13 (12.50 %) |
| Fulvestrant | 40 | 27.78 | 17 (45.5 %) | 13 (32.5 %) | 10 (25.00 %) |
| Cell free DNA (cfDNA) | |||||
| Total plasma cfDNA concentration (ng/μL) | 0.400 | 0.692 | 0.699 | ||
3.1. Real world PFS and OS: comparative analysis of palbociclib and ribociclib
Due to the unequal sample sizes and varying follow-up durations resulting from different approval dates, the analysis primarily focused on the PAL and RIB cohorts.
After a median follow-up of 36.4 months for PFS and 39.7 months for OS, no significant differences in PFS (p = 0.2573) (Fig. 1) or OS (p = 0.3783) (Fig. 2) were observed between PAL and RIB. However, a numerically longer median PFS was observed in the RIB group (45.7 vs 24.9 months), with higher PFS rates at both 12 months (77 % vs 69 %) and 24 months (60 % vs 45 %) compared to PAL. OS rates were comparable between the two groups at 12 months (97 % vs 96 %) and 24 months (87 % vs 84 %), with overlapping survival curves. Given the non-randomized nature of the study, additional analyses were performed to account for baseline imbalances. Univariable Cox models identified ET backbone, liver involvement, and serosal disease as potential prognostic factors (Supplementary Tables 1 and 3). These variables were included, together with treatment group, in multivariable Cox regression models. After adjustment for these covariates, the type of CDK4/6 inhibitor remained not significantly associated with either PFS or OS (Supplementary Tables 2 and 4), although effect estimates were directionally consistent with the patterns observed in the Kaplan–Meier curves.
Fig. 1.
Kaplan–Meier curves of progression-free survival in the MAGNETIC.1 cohort. After a median follow-up of 34.8 months, no statistically significant difference in PFS was observed between the two treatment groups.
Fig. 2.
Kaplan–Meier curves of overall survival in the MAGNETIC.1 cohort. Median OS did not differ significantly between groups, with overlapping survival probabilities at both 12 and 24 months.
3.2. Association between baseline biomarker levels and clinical characteristics
We next evaluated whether baseline levels of the investigated biomarkers were associated with disease burden or visceral involvement. No significant differences were observed between patients with visceral versus non-visceral disease for ESR1 promoter methylation (promA: p = 0.124; promB: p = 0.468) or for any ACTB fragment-based markers (ACTBshort: p = 0.905; ACTBmedium: p = 0.413; ACTBlong: p = 0.925) (Supplementary Table 5).
Similarly, no significant correlations were found between the number of metastatic sites and biomarker levels at baseline, dichotomizing metastatic burden as ≥3 versus <3 metastatic sites (Supplementary Table 5).
3.3. ESR1 promoter A (promA) and B (promB) methylation dynamics: differential epigenetic modulations across CDK4/6 inhibitors
Comparing matched samples between T0 and T3, PAL-treated patients showed a significant increase in promA methylation levels (33 vs 47.5 copies respectively, p = 0.0073). No significant changes in promB methylation were observed in PAL or RIB subgroups (Fig. 3, Fig. 4).
Fig. 3.
Longitudinal dynamics of ESR1 promoter A methylation in palbociclib-treated (A) and ribociclib-treated (B) patients. Changes in promA methylation levels assessed by ddPCR in plasma ctDNA at baseline (T0), first (T3), and second (T6) timepoints. A significant increase at T3 was observed in palbociclib-treated patients, followed by a significant reduction at T6.
Abbreviations: promA, ESR1 promoter A; ddPCR, droplet digital PCR; ctDNA, circulating tumor DNA; T0, baseline; T3, first evaluation; T6, second evaluation.
Fig. 4.
Longitudinal dynamics of ESR1 promoter B methylation in palbociclib-treated (A) and ribociclib-treated (B) patients. Changes in promB methylation levels assessed by ddPCR in plasma ctDNA at baseline (T0), first (T3), and second (T6) timepoints. PAL-treated patients exhibited a significant reduction between T0-T6 and T3-T6. No significant changes were observed in the RIB cohort.
Abbreviations: promB, ESR1 promoter B; ctDNA, circulating tumor DNA; T0, baseline; T3, first evaluation; T6, second evaluation.
Between T0 and T6, no significant changes emerged for RIB or PAL (Fig. 3, Fig. 4).
Between T3 and T6, PAL-treated patients showed a significant reduction in promA methylation (47.5 vs 33.5 copies respectively, p = 0.028), while no significant changes were observed for RIB. For promB, a significant decrease was observed exclusively in the PAL cohort (34 vs 13.5 copies respectively, p = 0.0013). (Fig. 3, Fig. 4).
3.4. Fragment-based analysis of ACTB fragments: distinct patterns
Regarding ACTBshort fragments, a significant decrease between T0 and T3 was detected for RIB (82.5 vs 52 bp, p = 0.017) (Fig. 5, Fig. 6A). Between T3 and T6, both groups demonstrated a significant rebound (15.75 vs 43.5 bp, p < 0.001 for PAL and 52 vs 66.50 bp, p = 0.014 for RIB) (Fig. 5, Fig. 6A). Comparing T0 and T6, only PAL-treated patients showed significantly higher levels of ACTBshort fragments (21.5 vs 43.5 bp, p < 0.001) (Fig. 5, Fig. 6A).
Fig. 5.
Fragmentomic analysis of ACTB fragments (ACTBshort in A, ACTBmedium in B, and ACTBlong in C) in palbociclib-treated patients.
Abbreviations: ACTBshort, short fragments; ACTBmedium, medium-length fragments; ACTBlong, long fragments; T0, baseline; T3, first evaluation; T6, second evaluation.
Fig. 6.
Fragmentomic analysis of ACTB fragments (ACTBshort in A, ACTBmedium in B, and ACTBlong in C) in ribociclib-treated patients. Kinetics of ACTBshort, ACTBmedium, and ACTBlong fragments in patients treated with RIB.
Abbreviations: ACTBshort, short fragments; ACTBmedium, medium-length fragments; ACTBlong, long fragments; T0, baseline; T3, first evaluation; T6, second evaluation.
For ACTBmedium fragments, between T0 and T3 both PAL and RIB showed a non-significant numerical reduction (Fig. 5, Fig. 6B). Between T3 and T6, a non-significant trend for decrease emerged in RIB-treated patients (Fig. 5, Fig. 6B). Over 6 months compared to baseline, only RIB-treated patients showed significantly lower ACTBmedium levels (4 vs 1 bp, p = 0.0039) (Fig. 5, Fig. 6B).
Considering ACTBlong fragments, a significant decrease from T0 to T3 was observed for both PAL (10.5 vs 6.5 bp, p = 0.0067) and RIB (27.75 vs 19 bp, p = 0.0019) groups (Fig. 5, Fig. 6C). Between T3 and T6, no significant increase was noted (Fig. 5, Fig. 6C). Comparing T0 and T6, only RIB-treated patients maintained a significant reduction in ACTBlong (27.75 vs 13.5 bp, p = 0.0021), whereas no significant changes were detected for PAL (Fig. 5, Fig. 6C).
4. Discussion
Current international guidelines recommend the combination of CDK4/6 inhibitors with endocrine therapy as the standard first-line treatment for HR+/HER2- MBC patients. However, intrinsic and acquired resistance remains an inevitable clinical challenge. Despite their established benefit in prolonging PFS, identification of reliable biomarkers to predict response or resistance is still lacking in routine practice. In this biomarker-driven analysis of the MAGNETIC.1 study, we focused on ctDNA-based epigenetic and fragmentomic profiling to investigate potential molecular signatures of resistance in patients treated with palbociclib or ribociclib.
Despite the predominance of palbociclib use, attributable to its earlier approval, no significant differences in PFS or OS were observed between palbociclib and ribociclib-treated patients after a median follow-up of 34.8 months. However, ribociclib-treated patients exhibited a numerically longer median PFS and higher survival rates at both 12 and 24 months, suggesting a potential clinical advantage. While this difference did not reach statistical significance, it is consistent with the results of prospective trials in which ribociclib demonstrated consistent improvements in OS, both in combination with fulvestrant in the MONALEESA-3 trial and with letrozole in MONALEESA-2, whereas palbociclib conferred only a non significant OS improvement in both PALOMA-3 and PALOMA-2 [23]. Recently, while the retrospective P-VERIFY study did not detect significant OS differences among CDK4/6 inhibitors combined with aromatase inhibitors [24], the prospective, multicenter PALMARES-2 study reported a survival benefit for ribociclib and abemaciclib compared to palbociclib across multiple endpoints, including real-world PFS (rwPFS), time to next treatment or death (TTNT-D), and time to chemotherapy or death (TTC-D) [25]. Preliminary OS results from PALMARES-2 similarly favored ribociclib and abemaciclib, although follow-up remains immature [25]. Such differences could reflect distinct pharmacodynamic properties, variability in trial design, or differences in patient selection criteria, underscoring the complexity of cross-study comparisons [23].
Importantly, our study extends beyond clinical outcomes by providing prospective ctDNA-based biomarker data.
Epigenetic dysregulation, particularly hypermethylation of ESR1 promoter regions, has been implicated in endocrine resistance. Here, matched-pair analyses revealed that palbociclib-treated patients experienced early promA methylation increases, followed by subsequent declines at later timepoints, a pattern not observed with ribociclib. This dynamic pattern suggests that early hypermethylation of ESR1 promoters might impair ESR1 binding and downstream signaling, potentially serving as an early predictor of reduced endocrine sensitivity, offering greater prognostic value than ESR1 mutation analysis alone. [26]. Our prior work demonstrated that early increases in ESR1 promoter methylation were consistently associated with significantly worse PFS and OS across all patient subgroups, underscoring the prognostic relevance of early methylation dynamics [16].
Although ESR1 mutations are a well-established mechanism of acquired resistance to aromatase inhibitors and monitoring their emergence can guide early treatment adaptation, as recently demonstrated in the SERENA-6 [27] and PADA-1 trials [28], their prevalence at baseline in our cohort was low, with only 8 patients (7 %) carrying a detectable ESR1 alteration. This limited the feasibility of performing robust comparative analyses across subgroups. Longitudinal ESR1 mutation profiling of all available timepoints is currently ongoing and will enable future integration of genetic, epigenetic, and fragmentomic biomarkers to refine resistance stratification.
Fragmentomic analysis further distinguished treatment responses: a rebound in ACTBshort fragments levels at six months, compared to baseline, was uniquely observed in palbociclib-treated patients. We previously demonstrated that elevated baseline levels of ACTBshort, as well as a rise exceeding 20 % between baseline and the first evaluation, were significantly associated with worse PFS and OS. These findings suggest that increased tumor DNA shedding may reflect more aggressive disease biology [20,29]. Consequently, the observed increase in ACTBshort levels at 6 months could potentially serve as a surrogate biomarker for treatment resistance. However, this observation should be interpreted with caution, as the present data capture longitudinal signal dynamics rather than providing direct evidence of mechanistic differences between CDK4/6 inhibitors. Therefore, the observed changes in ACTBshort levels reflect treatment-associated ctDNA kinetics and cannot be assumed to indicate distinct biological effects of specific agents. The clinical usefulness of these biomarkers will depend on evaluating more longitudinal timepoints, which may offer the opportunity to detect molecular progression prior to radiographic progression and thus refine treatment adaptation strategies. Importantly, the clinical relevance of ctDNA-based biomarkers is tightly linked to sampling timing. In the SERENA-6 study, the median interval from initiation of AI plus CDK4/6i therapy to randomization was 23.1 months [27]. Conversely, our findings also underscore the need to validate rapid-detection strategies, as even a 3-month interval may be too delayed to inform timely therapeutic decisions. In this context, the BioItaLEE study demonstrated that early serum Thymidine kinase 1 activity (sTKa) dynamics assessed at day 15 of cycle 1 and at day 1 of cycle 2 have significant prognostic value [30]. Future studies should therefore prioritize earlier and more frequent longitudinal sampling to determine whether fragment-based ctDNA dynamics can support real-time treatment adaptation.
In parallel, the dynamics of ACTBmedium and ACTBlong fragments should be considered, as these predominantly originate from leukocyte derived genomic DNA rather than tumor cells. The more pronounced suppression of ACTBmedium and ACTBlong observed in RIB-treated patients may reflect treatment related effects on leukocyte turnover or other pharmacodynamic influences, although this interpretation remains speculative.
A further point concerns our choice of ACTB as the locus for fragment size analysis. ACTB is one of the most extensively characterized housekeeping genes and is widely used across molecular assays precisely because of its stable behavior across biological contexts and experimental conditions. While the inclusion of multiple housekeeping genes could theoretically broaden the fragmentomic assessment, this approach would substantially increase assay complexity and cost, and several commonly used housekeeping genes exhibit context-dependent variability. Using a single, well-characterized gene such as ACTB therefore minimizes technical variability while allowing a robust and interpretable estimation of cfDNA fragmentation patterns within the constraints of a prospective clinical study.
Despite these promising insights, several limitations must be acknowledged. First, although the MAGNETIC.1 study was prospective, its non-randomized and non-blinded design may have introduced selection and observational biases. Biomarker analyses were restricted to palbociclib and ribociclib due to sample size constraints, and the relatively small sample size and homogeneity of the study population necessitate validation of this approach on a larger scale with better representation of different subgroups. Moreover, for some patients, follow-up is still in its early stages, potentially underestimating the prognostic impact of the biomarkers assessed. The analysis of subsequent timepoints, particularly those immediately preceding and concurrent with disease progression, will be essential to better understand the biological behavior and temporal dynamics of circulating actin fragments. In addition, the fragmentomic analyses presented here should be interpreted as hypothesis-generating, given the limited genomic scope of the assay and the potential influence of pre-analytical variables that cannot be fully excluded despite standardized processing. These methodological constraints further reinforce the need for broader, genome-wide approaches and external validation to clarify whether the observed patterns reflect true mechanistic differences or treatment-related biological noise, and such analyses are currently underway.
5. Conclusion
This biomarker-focused analysis underscores the potential value of integrating ctDNA-based epigenetic and fragmentomic profiling into the management of HR+/HER2– metastatic breast cancer. While the treatment-specific patterns observed here are intriguing and support the candidacy of these biomarkers for further exploration, their clinical relevance remains preliminary. Larger, well-controlled validation studies will be required to determine whether these molecular signatures hold consistent prognostic or predictive value and whether they can ultimately contribute to personalized treatment strategies in this setting.
CRediT authorship contribution statement
Claudia Noto: Writing – review & editing, Writing – original draft, Conceptualization. Lorenzo Foffano: Writing – review & editing, Writing – original draft, Methodology, Formal analysis, Data curation, Conceptualization. Fabiola Giudici: Writing – review & editing, Software, Investigation, Formal analysis, Data curation. Elisabetta Molteni: Writing – review & editing, Validation, Methodology, Investigation, Formal analysis. Alessandra Franzoni: Writing – review & editing, Validation, Resources, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Martina Tessitori: Writing – review & editing, Writing – original draft. Linda Cucciniello: Writing – review & editing, Writing – original draft. Silvia Bolzonello: Writing – review & editing, Investigation. Lucia Da Ros: Writing – review & editing, Investigation. Lucia Bortot: Writing – review & editing, Resources. Elena Scudeler: Writing – review & editing, Writing – original draft. Brenno Pastò: Writing – review & editing, Resources. Giulia Cudia: Writing – review & editing, Resources. Serena Della Rossa: Writing – review & editing, Resources. Marta Bonotto: Writing – review & editing, Resources. Alessandro Marco Minisini: Writing – review & editing, Supervision, Resources. Giuseppe Damante: Writing – review & editing, Resources. Barbara Belletti: Writing – review & editing, Supervision, Resources. Lorenzo Gerratana: Writing – review & editing, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. Fabio Puglisi: Writing – review & editing, Supervision, Resources, Project administration, Methodology, Funding acquisition, Conceptualization.
Ethics approval and consent to participate
The study was approved by the ethics committee under the CEUR-2018-Sper-056-CRO protocol. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article. The study was performed in concordance with the Health Insurance Portability and Accountability Act and the Declaration of Helsinki.
Availability of data and materials
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Funding
The present work was supported by the Italian Ministry of Health – Ricerca Corrente. This study was also supported by AstraZeneca and the Ministry of Health Ricerca Finalizzata grant (Grant Number: RF-2016-02362544) to FP; the CRO Aviano 5x1000 2014, redditi 2013 Cancer Specific Intramural Grant to LG; the Associazione Italiana per la Ricerca sul Cancro (AIRC) grant (IG#20061) and Ministry of Health Ricerca Finalizzata grant (Grant Number: RF-2021-12371961) to BB; The funders were not involved in the study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication.
Competing interests
M.B. reports advisory/consultancy fee from AstraZeneca, Lilly, MSD, Novartis, Pfizer, SeaGen; travel grants from Lilly, Roche. A.M. reports advisory/consultancy fee from Novartis, MSD, BMS, Merk, Sunpharma, PierreFabre, Gilead, Seagen, Genomic Health; invited speech from Novartis, MSD, BMS, Merk, Sunpharma, Sanophi, PierreFabre, AstraZeneca, Daiichi Sankyo; travel grants from Gilead, PierreFabre; other familial: MSD, AstraZeneca, Pharmamar, GSK. L.G. reports advisory/consultancy fee from AstraZeneca, Daiichi Sankyo, Eli Lilly, GlaxoSmithKline, Incyte, Novartis, Pfizer, Merck Sharp & Dohme, Menarini Stemline, Abbvie; research funding from Menarini Silicon Biosystems. F.P. reports honoraria for advisory boards, activities as a speaker, travel grants, research grants from Amgen, Astrazeneca, Daiichi Sankyo, Celgene, Eisai, Eli Lilly, Exact Sciences, Gilead, Ipsen, Italfarmaco, Menarini, MSD, Novartis, Pierre Fabre, Pfizer, Roche, Seagen, Takeda, Viatris; Research funding from Astrazeneca – Eisai – Roche.
Footnotes
Supplementary data to this article can be found online at https://doi.org/10.1016/j.breast.2026.104703.
Appendix A. Supplementary data
The following is the Supplementary data to this article:
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.






