ctDNA and tumor-based biomarkers of giredestrant response in acelERA breast cancer

Domain: biology
Published: 2026-03-12
Editorial outcome: minor_revision

Abstract
Endocrine therapy (ET) resistance in estrogen receptor positive (ER+) advanced breast cancer is often linked to ESR1 mutations, yet responses to oral selective ER degraders vary within mutant subgroups. Through a biomarker analysis of acelERA Breast Cancer (NCT04576455), we show that tumor ER transcriptional activity as well as circulating tumor DNA (ctDNA) genomics and dynamics effectively stratify response to ET, including giredestrant. We find that following first-line therapy, the ctDNA genomic landscape is diverse and influenced by CDK4/6 inhibitor exposure. Despite this complexity, ER activity in ESR1-mutant tumors remains comparable to early breast cancer but is reduced in most non-mutant cases. This maintained ER activity is associated with giredestrant benefit. Furthermore, early ctDNA clearance identifies responding patients, and the combination of low ER activity and high ctDNA burden predicts rapid clinical progression. These findings provide a framework for personalizing future breast cancer therapies by integrating liquid biopsies with tissue-based signatures.

Introduction
Approximately 70% of diagnosed breast cancer cases are estrogen receptor-positive (ER+) and are largely fueled by estrogen1,2. Therefore, targeting the ER pathway has been a successful strategy for therapeutic intervention for a large proportion of patients with breast cancer3. Despite notable advances, resistance to endocrine therapy (ET) often emerges, necessitating the development of new therapeutics such as selective estrogen receptor antagonists and degraders (SERDs)4–6. Oral SERDs have recently emerged as promising substitutes to currently approved ETs7–9 and the intramuscularly injected SERD fulvestrant, offering potential advantages in efficacy, with improved administration methods and bioavailability. Despite these possible advantages, data on oral SERD efficacy and associated biomarkers remains limited.

Mutations in the estrogen receptor 1 (ESR1) gene, which encodes the ER protein, have emerged as potential biomarkers of ET resistance as well as responsiveness to oral SERDs10. These alterations, enriched in the advanced setting and most commonly acquired following aromatase inhibitor (AI) therapy, confer ligand-independent receptor activation to promote breast cancer progression11,10. Recent phase 2 and 3 clinical trials in advanced breast cancer (aBC) have consistently shown that patients with ESR1 mutant (ESR1m) tumors generally show a more pronounced response to single agent oral SERDs compared to standard-of-care ETs12–15.

Here, we present an exploratory biomarker analysis of the phase 2 acelERA Breast Cancer (BC) study (NCT04576455), which compared giredestrant to physician’s choice of ET (PCET) in 2L/3L ER+, HER2- locally advanced or metastatic breast cancer (La/mBC) patients. Giredestrant is a highly potent, nonsteroidal, orally bioavailable small molecule SERD and full ER antagonist that achieves robust ER occupancy and is designed to drive deep and sustained inhibition of ER signaling, both ligand-dependent and ligand-independent16–18. Giredestrant competitively binds wild-type and mutant ER, blocking co-activator recruitment and immobilizing the receptor to suppress activity and reduce protein levels. As previously reported15,19, acelERA BC did not reach statistical significance for the primary endpoint of investigator-assessed progression-free survival (INV-PFS), but giredestrant exhibited a numerical improvement over PCET as noted by ~20% relative reduction in risk of progression or death and favorable results for secondary efficacy endpoints. Giredestrant also demonstrated a more pronounced benefit in patients harboring detectable ESR1m, with a hazard ratio (HR) of 0.60 (95% CI, 0.35 to 1.03).

This study aims to uncover additional indicators of ET (giredestrant, fulvestrant, and AI) response, including biomarkers derived from circulating tumor DNA (ctDNA). ctDNA has been demonstrated as a valuable non-invasive predictive and prognostic biomarker across many indications20, providing insights into tumor heterogeneity14,21,22, therapy response monitoring23–25, relapse detection26,27, and identification of resistant cell populations26. Exploratory biomarker analyses from this study will aid in the development of future strategies for patient selection, clinical trial design, and treatment monitoring for oral SERDs.

Results
Baseline ctDNA landscape of 2L/3L ER+ HER2- La/mBC patients
Baseline plasma from 229 of 303 acelERA patients was evaluated with FoundationOne® Liquid CDx (F1LCDx), which sequences >1.25 Mb of genomic content covering 324 genes (Fig 1a). Additional F1LCDx testing was performed at cycle 2 day 1 (C2D1, n=220) and end-of-treatment (EOT, n=155). Most PCET arm patients (~75%) received fulvestrant, with the remainder receiving an AI. For subjects enrolled in China, the PredicineCARE assay was used to determine ESR1m status (N=3).The following analyses focus exclusively on F1LCDx results.

ctDNA tumor fraction (TF) was defined as the proportion of cell-free DNA originating from cancer cells relative to the total amount of cell-free DNA present in a given plasma sample. At baseline, ctDNA was detected in 78% (179) of evaluable samples, with a median TF of 7.7% overall and 14% in patients with samples above the limit of detection (Fig. 1a-b, not determined with PredicineCARE assay). There was no significant difference in baseline ctDNA TF levels or ESR1m allele frequency (MAF) between treatment arms, regardless of ESR1m status (Fig. 1b, Supplementary Fig. 2a). Among the samples where ctDNA was detected, 98% (176) harbored at least one pathogenic (known or likely to affect protein function) SV, the majority of which were substitutions of unknown significance (Fig. 1c-d). Pathogenic mutations in ESR1 were the most prevalent alterations detected (49.2%), followed by PIK3CA (48.6%), TP53 (37.4%), and RB1 (15.1%) (Fig. 1e). Copy number alterations were detected in 36% of samples, primarily involving FGF19/3/4, CCND1, and FGFR1, likely from the same amplicon (Supplementary Fig. 1a-b). Pathogenic rearrangements were observed in 16% of samples, most commonly as one or two large truncations or deletions not enriched for a particular gene. (Supplementary Fig. 1c-d). ESR1 and PIK3CA SVs co-occurred in ~15% of samples with F1LCDx results (Supplementary Fig. 1e).

We identified 183 pathogenic ESR1 mutations (known or likely to affect ER protein function) in 88 patients. The most common variants were Y537X (X denotes any amino acid, 64%) and D538G (60%), E380Q (22%), and L536X (22%) with 54% of patients harboring multiple ESR1 mutations (Fig. 1f-g, Supplementary Fig. 2c). Most ESR1 mutations (73%) were subclonal (Fig. 1h). There was no difference in the number of ESR1m per patient based on whether a patient had received 1 or 2 prior lines of therapy (Supplementary Fig. 2d). Patients with ESR1m also had a lower prevalence of PIK3CA and CDH1 pathogenic alterations (Fig. 1i).

In the study cohort, 41% (125/303) of patients received a prior CDK4/6 inhibitor (i), while the remainder had received prior AI or other ET without CDK4/6i exposure. As 1st line treatment with CDK4/6 inhibitors in the advanced setting has been shown to result in altered genomic profiles compared to AI alone11, this presented a unique opportunity to isolate specific biomarkers associated with CDK4/6i resistance. Prior CDK4/6i treatment was associated with ESR1 mutations, which were twice as prevalent in CDK4/6i-experienced patients and also greater in number per patient compared to CDK4/6i-naive patients (Fig. 1j-k). RB1 alterations were three times more common post-CDK4/6i, consistent with known resistance mechanisms28 (Fig. 1j). ESR1 mutant allele frequency (MAF) appeared consistent regardless of prior CDK4/6i treatment (Fig. 1l), but ctDNA rates were higher in patients with prior CDK4/6i therapy (Fig. 1m-n).

ctDNA biomarkers associated with response to endocrine monotherapy
Previous work examined the association of INV-PFS with baseline demographics in acelERA15. Consistent with other studies evaluating oral SERDs12–14, we found that the presence of an ESR1m correlated with an improved response to giredestrant versus PCET (Fig. 2a, Supplementary Fig. 3a). This benefit was statistically significant for patients with disease harboring a single ESR1m, and showed a similar trend with multiple ESR1 mutations (independent of ctDNA tumor fraction, Fig. 2b, Supplementary Fig. 3a). Giredestrant showed PFS improvement over PCET if a D538G (HR 0.49, mPFS 5.4 vs. 2.8 months) or Y537X (HR 0.60, mPFS 4.3 vs. 2.8 months) ESR1 variant was detected (Supplementary Fig. 3a, note that many patients had more than one variant).

When looking specifically in patients with ESR1m disease, giredestrant trended toward a favorable benefit in many subgroups, particularly patients with PIK3CAm tumors, high tumor ER transcriptional activity, or patients with no prior CDK4/6i exposure (Supplementary Fig. 3b). As such, the acelERA trial protocol included both ESR1m status and prior CDK4/6i treatment as stratification factors, recognizing their potential impact on treatment outcomes. As reported previously, giredestrant did not show significant improvement over PCET based on a patient’s prior CDK4/6i exposure and acelERA was not powered to ask this question15. However, when looking across subgroups based on both ESR1m and CDK4/6i experience, giredestrant showed a near 3-fold PFS improvement over PCET among patients with ESR1m, CDK4/6i naive tumors (HR 0.34, mPFS 11.1 vs. 3.8 months, Fig. 2c-d, Supplementary Fig. 3a-b), which likely have less evolved resistance mechanisms and reduced genomic complexity/clonal heterogeneity compared to CDK4/6i-exposed patients. Within the giredestrant arm, outcomes were similar regardless of ESR1m status (Fig. 2e).

Given that ESR1 mutations alone did not fully predict treatment response, we investigated whether additional genomic features and ctDNA levels might better identify patients likely to benefit from ET. Beyond ESR1m, we found baseline ctDNA detection and high TF levels were prognostic regardless of treatment arm (Supplementary Fig. 4a-c). Baseline ctDNA TF was significantly higher in patients who had confirmed progressive disease (PD) (Fig. 2f). All partial responses (PRs) in the PCET arm occurred only when ctDNA was undetectable, whereas giredestrant produced PRs across a range of tumor fractions (0-35%, median 7%, Fig. 2f). In either arm, PFS was improved nearly 4-fold in patients without detectable ctDNA (HR 0.33, mPFS 12.75 vs. 3.81 months) (Fig. 2g, Supplementary Fig. 4a-c) and patients with high baseline TF (>20%) had particularly poor outcomes (Fig. 2h, Supplementary Fig. 4a-c).

Additional genomic features showed treatment-specific associations. RB1 mutations predicted poor response regardless of treatment, while ESR1 mutations were negatively associated with PCET response (Supplementary Fig. 4a-b). Giredestrant outperformed PCET in patients with PIK3CAm disease or tumors without detectable RB1 mutations (Supplementary Fig. 3a). Patients with co-occurring ESR1m and PIK3CAm disease (n=35) saw particularly improved PFS with giredestrant (HR 0.38, mPFS 5.6 vs. 1.8 months). These results highlight the prognostic value of baseline ctDNA levels and identify specific genomic features predictive of response to different ETs.

Given the robust prognostic value of ctDNA, we examined clinical features associated with the 22% (50) of patients without detectable baseline ctDNA. These patients were more likely to be non-white, have higher BMI, no liver metastases, lower LDH, and no prior CDK4/6i exposure (Supplementary Fig. 5a). Only 26% of ctDNA-negative patients had prior CDK4/6i exposure, compared to 51% of ctDNA-positive patients. Tumors from patients with detectable ctDNA (N=134), with presumably higher rates of ctDNA shedding, had lower levels of estrogen response signatures compared to those without detectable TF (N= 35, Supplementary Fig. 5b). We found that ctDNA detection rates, PFS outcomes, and on-treatment TF dynamics were similar between ductal and lobular breast cancer (Supplementary Fig. 6a-e), supporting ctDNA's utility across histological subtypes.

Tumor ER activity is a predictor of giredestrant response
Given that gene expression patterns can reveal functional pathway activities not captured by mutation status alone, we next examined tumor transcriptional profiles. RNAseq was performed on fresh or archival tumor tissue from 195 patients, collected prior to the initiation of study therapy (Fig. 1a). Of note, the majority of patients enrolled in the study were considered to have “ER positive” (>10% cells) tumors, and only 7/303 were “ER low” (1-10% cells) based on prior immunohistochemistry (IHC). First, without considering prior therapies of each tumor, we found that the early estrogen response hallmark pathway was significantly correlated with giredestrant PFS (Supplementary Fig. 7a). ER transcriptional activity, quantified by measuring the expression of known E2 repressed and induced genes18, was significantly higher in tumors that exhibited stable disease (SD) or PR with giredestrant (Fig. 3a); PCET showed a similar direction of effect but did not reach significance. A non-significant ER activity × treatment arm interaction (p = 0.946 in Cox model and p=0.344 in two-way ANOVA) suggests ER activity’s predictive effect may apply across ETs, though this study was not powered for interaction analysis.

Patients with high ER tumor activity (z-score >0) or more luminal-like tumors (defined by TCGA-derived transcription factor scores29) also showed longer PFS specifically with giredestrant (median 7.5 vs 3.6 months; Fig. 3b). The PCET arm showed a similar but non-significant separation (5.6 vs 5.1 months; Supplementary Fig. 7b). Tumors with high ER activity also had increased expression of cell cycle genes (Supplementary Fig. 7c). Thus, elevated ER activity functions as a prognostic marker of endocrine sensitivity and may predict patients most likely to benefit from giredestrant.

A key caveat is that many tumors were scored from archival biopsies; if ER activity declined during intervening treatments, some “ER-high” cases were probably mis-classified as such, dampening the true benefit signal and confounding any direct comparison between giredestrant and PCET. We therefore categorized tissue samples based on biopsy timing: 97 tumors were treatment naive, 54 were collected after neoadjuvant or adjuvant therapy but prior to 1L metastatic therapy, and 32 tumors were collected after 1L progression (“post-1L”). The majority (80%) of tumors collected after adjuvant therapy had been exposed to ET, ranging from 6-78 months. Duration on adjuvant ET was not significantly associated with tumor ER activity. ER activity progressively decreased through lines of therapy (Fig. 3d), suggesting therapy-induced acquisition of ER-independence. A trend toward longer PFS in the ER high group with giredestrant was consistent across biopsy timings (Supplementary Fig. 7d-f; trend was not apparent with PCET, Supplementary Fig. 7g). Post-menopausal patients showed significantly lower ER activity than premenopausal patients (Supplementary Fig. 7h). Half of the post-1L tumors had been previously exposed to CDK4/6i (blue dots, Fig. 3d) but this did not appear to be associated with ER activity.

After exposure to 1st line therapy, ESR1nmd tumors exhibited significantly lower ER activity compared to ESR1m tumors (Fig. 3d). ESR1m tumors were enriched for Myogenesis, hypoxia, and wnt/beta-catenin signaling as well as the Luminal B PAM50 and the integrative cluster (IC) IC3 and IC4 molecular subtypes, consistent with higher ER activity, higher levels of genomic instability, and less favorable outcomes30,31,32 (Supplementary Fig. 7i-k). Importantly, a subset of ESR1nmd tumors retained high ER activity and achieved clinical benefit (CR, PR, or stable disease for ≥ 6 months, Fig. 3e), while approximately half of ESR1m tumors showed low ER activity and did not benefit from treatment. PIK3CA and RB1 alterations were enriched in “non-responsive” ESR1nmd tumors with low ER activity (Supplementary Fig. 7l). These findings suggest that tumor ER activity declines through treatment lines, and that ESR1m testing alone is insufficient to identify all patients who may benefit from an oral SERD. The combination of ESR1m status and tumor ER activity may provide more comprehensive prediction of treatment response.

ctDNA dynamics
While baseline genomics can identify prognostic and predictive biomarkers, analysis of longitudinal ctDNA dynamics provides a real-time evaluation of treatment response. To analyze changes in ctDNA during acelERA, we compared baseline and available matched samples at C2D1 (on treatment, N=208 matched) and EOT (N=134 matched). Overall, 25% (22/89) of patients in the giredestrant arm and 11% (8/75) of patients in the PCET arm switched from ctDNA detected to undetected on treatment. A reduction in ctDNA TF greater than 75% (a cutoff established previously23) was associated with improved PFS, and this was significantly more pronounced with giredestrant than PCET (Fig. 4a). ctDNA dynamics appeared to be prognostic independent of the baseline tumor fraction; patients achieving >75% TF reduction had similar baseline TF levels to those who did not, with comparable reduction rates across high and low baseline TF groups. Further, when adjusting for baseline TF in a regression model, a >75% reduction in ctDNA was still significantly predictive of PFS. Patients with PR had lower ctDNA TF levels on-treatment compared to patients with SD or PD (Fig. 4b). ctDNA TF decreased to a greater degree by C2D1 with giredestrant (median -66%) compared to PCET (median -29%) or the fulvestrant subset (median -36%) (Fig. 4c). At EOT, PCET-treated patients, including the fulvestrant subset, showed median TF increases (+20% and +17%, respectively) consistent with progression, while giredestrant-treated patients maintained a median -47% reduction (Fig. 4c). Giredestrant produced greater ctDNA TF declines in ESR1m patients (-80%) versus ESR1nmd (+5%), a difference not observed with PCET or fulvestrant (Fig. 4d). Similarly, and although small numbers prevent statistical conclusions, post-1L ER high tumors saw a greater median ctDNA TF decline on treatment (-60%) compared to ER low (+3%, Supplementary Fig. 8a). Patients treated with giredestrant and clonal ESR1m also saw greater median levels of TF decline (-82%) compared to those with subclonal ESR1m (-48%), suggesting influence of tumor heterogeneity (no statistics due to low sample size, Fig. 4e).

ctDNA detection at C2D1 was prognostic regardless of treatment arm or prior CDK4/6i exposure (Fig. 4f and Supplementary Fig. 8b). ctDNA was undetectable at C2D1 in 66% of patients with clinical benefit and 75% with confirmed response, versus only 15% without clinical benefit and 28% without confirmed response. These patterns were consistent in both treatment arms. Further, ctDNA detection at C2D1 effectively risk-stratified patients that exhibited stable disease at the 8 week (C3D1) tumor scan, providing valuable granularity to image-based response metrics (HR: 0.30 CI: 0.15-0.63, Supplementary Fig. 8c). The predictive value of ctDNA detection for PFS events changed over time (Supplementary Fig. 8d), with C2D1 detection showing ~75% positive predictive value for progression within 6 months, while undetectable ctDNA had ~75% negative predictive value. These findings demonstrate the prognostic value of early on-treatment ctDNA assessment, though the 25% progression rate in ctDNA-negative patients suggests continued monitoring is warranted. This highlights the clinical utility of early on treatment ctDNA testing to evaluate treatment response in late-stage breast cancer, and further pinpoint the anti-tumor efficacy of giredestrant in tumors with high ER activity.

ESR1 ctDNA dynamics
We next examined ESR1m dynamics in ctDNA to evaluate giredestrant's impact on ESR1 mutant alleles versus PCET. With giredestrant, ESR1m prevalence declined from 51% to 40% of patients on-treatment and remained stable at EOT, while PCET-treated patients showed an increase from 47% to 53% at EOT. Six giredestrant-treated patients developed new pathogenic ESR1 mutations at EOT (D538G, Y537C/N/S, H536D/Y), along with alterations in TP53, RB1, PIK3CA, and SMAD4, highlighting potential resistance mechanisms driving disease progression. Several ESR1 variants of unknown significance were also detected at EOT, primarily glutamate-to-aspartate substitutions in the ligand binding domain. ESR1 MAF decline on treatment was significantly greater with giredestrant compared to PCET or fulvestrant at both timepoints (Fig. 4g) and patients with response consistently showed near-complete ESR1 MAF clearance (Supplementary Fig. 8f-g). Based on preclinical and clinical evidence suggesting variant-specific effects on ER antagonist efficacy 33–35, we examined whether individual ESR1 mutations responded differently to each therapy. It should be noted, as described in Figure 1, the majority of ESR1 mutations co-occurred alongside another ESR1m.While D538G, E380Q, L536X, and Y537X mutations showed similar patterns of clearance with giredestrant, D538G and Y537X demonstrated significantly greater reduction compared to PCET. (Fig. 4h). Together these results suggest that giredestrant is more effective in clearing tumor cells harboring ESR1 mutations compared to AIs or fulvestrant, and provide clinical evidence supporting the mechanistic understanding that these constitutively active ESR1 mutations require an oral SERD approach.

Clinical, ctDNA, and tumor features associated with rapid clinical progression on ET
We observed a sharp initial PFS dropoff in both treatment arms, with 29% (87/303) of patients progressing within 3 months ("rapid progression" or RP), balanced across arms and regardless of ESR1m status or prior CDK4/6i exposure (Fig. 5a-b, Supplementary Fig. 9a-c). This “rapid progression” (RP) phenomenon has been noted in other late line ER+ patients receiving ET, including oral SERDs12–14. To identify patients unlikely to respond to ET in this setting, we performed an unsupervised analysis of baseline genomic factors which revealed that baseline ctDNA TF and lower tumor ER activity were significantly associated with RP (Supplementary Fig. 9d). Clinical features associated with RP included prior CDK4/6i therapy, duration of prior therapy, liver metastases, and metabolic features such as lower baseline BMI (Supplementary Fig. 9d). While prior CDK4/6i exposure was prognostically unfavorable, 40% of RP patients (N=34) were CDK4/6i-naïve, suggesting this feature alone is not sufficient to predict RP. Similarly, approximately 40% of ESR1m patients exhibited RP, indicating ESR1m status alone cannot predict giredestrant response (Fig. 5a, Supplementary Fig. 9a-c). Genomically, RP patients showed higher prevalence of RB1 and CDH1 mutations (Fig. 5c), while ESR1m patients with RP specifically had higher PIK3CA and lower GATA3 mutation rates (Fig. 5d). Baseline ctDNA TF was significantly higher in patients with RP compared to non-RP, consistent with our observation that baseline TF is poorly prognostic (Fig. 5e-f). On treatment, TF increased in RP patients (+9%) but decreased substantially in non-RP patients (-69%), reinforcing the prognostic value of TF clearance (Fig. 5g), though ESR1 MAF decline was similar between groups (Fig. 5h). These findings indicate higher baseline ctDNA TF and lack of on-treatment ctDNA clearance are associated with rapid progression. Unlike ESR1m status, lower ER tumor activity predicted RP specifically in giredestrant-treated patients, but not PCET (Fig. 5i). Transcriptionally, tumors from RP patients showed decreased estrogen response gene expression and increased EMT, with modest elevations in angiogenesis, Wnt/Hedgehog/β-catenin, and adipogenesis signaling (Fig. 5j).

Lastly, using this rich dataset, we developed a multivariate model for predicting rapid clinical progression. A conservative logistic elastic net model (alpha = 0.5 and lambda.1se = 0.169) was used to assess the contribution of 25 clinical and genomic features with rapid progression on n = 159 subjects with non-missing data. Binary ER activity score, prior CDK4/6i exposure, presence of liver metastases, and detection of ctDNA by F1LCDx were the variables with non-zero coefficients, which collectively predicted RP with an AUC of 0.79 (Fig. 5k-l).

Methods
Ethics & Inclusion Statement
This retrospective study adhered to the REMARK guidelines. The study did not result in any stigmatization, incrimination, or discrimination to participants. acelERA BC was performed in accordance with Good Clinical Practice guidelines and the Declaration of Helsinki. Protocol approval was obtained from an independent ethics committee for each participating site. Every patient gave written informed consent.

Patient population
acelERA study design, patients, oversight, and assessments were described previously15. Briefly, the patient population was post- or pre-/perimenopausal women or men, ≥ 18 years with measurable or bone-only disease who progressed after 1-2 lines of systemic therapy. Patients were randomized 1:1 to giredestrant or fulvestrant/API and treated until disease progression or unacceptable toxicity. Patients were stratified by visceral disease, prior CDK4/6i, and prior fulvestrant. Prior treatment with an investigational SERD was not allowed. The clinical cutoff date used for these analyses was July 15, 2022. Clinical benefit rate (CBR) was defined as a confirmed complete or partial response or stable disease for ≥ 6 months. Overall response rate (ORR) was defined as a confirmed complete or partial response. As reported previously, 302/303 participants in acelERA were female, so biomarker data from a single male participant are included throughout this manuscript. Due to the insufficient sample size, sex-based analyses were not reported.

Evaluation of plasma samples
F1LCDx was performed in the Foundation Medicine Inc. laboratory, according to standard workflow46. Plasma was separated from whole blood by centrifugation and cfDNA was isolated with a KingFisher™ Flex Magnetic Particle Processor (Thermo Fisher, Waltham, MA, USA) and constructed into genomic libraries. Libraries were sequenced on the Illumina NovaSeq 6000 platform (Illumina Inc., San Diego, CA, USA). Samples with sufficient cfDNA content (lowest cfDNA input mass yielding successful results was 24.34 ng), as assessed by the Agilent 4200 TapeStation assay, were used for further analyses. For longitudinal analysis of changes in ctDNA levels, results were reanalyzed for computational bridging to FoundationOne®Monitor, an assay intended to report ctDNA tumor fraction quantity longitudinally for monitoring response to therapy.

Pathogenic short variants in this study were defined as short nucleotide variants with known or likely impact on protein function (per COSMIC). ctDNA tumor fraction (TF) quantification provides an estimate of the percentage of ctDNA present in a cfDNA sample47. The TF algorithm used a multi-omic assessment from cell free DNA and filtered suspected clonal hematopoiesis-derived alterations algorithmically. All samples with ctDNA TF detected (TF >0) were considered “TF-detected”. The lowest ctDNA TF value detected and quantified across all samples was 0.15%. Patients with ctDNA detected but not quantifiable, as well as patients without detectable ctDNA at both time points were excluded from percent change analyses. Variant clonality was calculated as MAF divided by TF, and variants with MAF/TF <0.25 were considered sub-clonal. These analyses were performed by Foundation Medicine as a part of the F1LCDx data output. ESR1 mutation status for patients enrolled in China was assessed using the PredicineCARE liquid biopsy assay. ESR1 mutations were classified as 'clonal' only if all detected ESR1 mutations were clonal. Conversely, samples were categorized as 'subclonal' if at least one subclonal ESR1 variant was present, regardless of whether additional clonal variants of the same or different ESR1 mutations were identified.

The PPV and NPV of ctDNA detection in predicting PFS events were estimated at multiple intervals after the C2D1 F1LCDx test using the cumulative incidence function estimator. The time to a PFS event after F1LCDx was defined as the number of months from C2D1 to a PFS event, censored at the last follow-up. Clustered bootstrapping was used to account for the nonindependence of multiple tests per patient.

Baseline TF and the categorical variable of whether a patient experienced 75% clearance were tested for their relationship with PFS by fitting a cox proportional hazards regression model with either each as a single variable or with both together (PFS ~ baseline TF + 75% clearance) in the subset of patients with both data types. This was performed in R using coxph() [survival v3.8-3].

Tumor RNAseq
Tumor RNAseq was performed by Q2 Lab Solutions using the RNA Access Library Preparation Kit (Illumina) followed by paired-end (2×50b, 50M reads) sequencing on the HiSeq platform (Illumina). RNASeq reads were aligned to the human reference genome (NCBI Build 38) using GSNAP (Wu and Nacu, 2010; Wu et al., 2016) version 2013-10-10, allowing a maximum of two mismatches per 75 base sequence (parameters: ‘-M 2 -n 10 -B 2 -i 1 -N 1 -w 200000 -E 1-pairmax-rna = 200000 –clip-overlap). To quantify gene expression levels, the number of reads mapped to the exons of each RefSeq gene was calculated using the functionality provided by the R/Bioconductor package GenomicAlignments. Raw counts were normalized to transcripts-per-million (TPM). ER z-scores were calculated using a predefined, experimentally derived set of 38 ER target genes18 normalized to a reference set of ER+ breast cancer samples. Voom was used to identify differentially expressed genes and generate count tables from raw reads which were used to derive PAM50 and IC1032 subtypes. For gene pathway analysis, hallmark gene sets were downloaded from MSigDB (http://www.gsea-msigdb.org/gsea/msigdb/human/collections.jsp#H; v2022.1.Hs) and enrichment scores were determined using gsva (R package: GSVA [v1.50.5]; options min.sz=2, max.sz=1000). For analyses of hallmark signatures with PFS, each signature was evaluated for association with PFS with a Cox proportional-hazards model (Cox et al., 1972) using the coxph function (R package: survival [v3.8-3]), and significance was assessed also with this function using a Wald test. For association of hallmark signatures with categorical variables such as ESR1 mutation status or rapid progression, we fit a univariate linear model using lmFit and then applied empirical Bayes moderation (R package limma [v3.58.1]). Forest plots for visualization were generated using the forest_model function (R package forestmodel [v0.6.2]). Heatmaps for visualization were generated using the Heatmap function (R package ComplexHeatmap [v2.18.0]). Dot plots created with ggplot (R package ggplot2 [v3.5.1]) and data manipulation assisted by R package dplyr [v1.1.4].

Statistics and Reproducibility
ctDNA data (other than ESR1 mutation status) from patients with PredcineCARE results were excluded from most analyses in this manuscript as this assay did not provide tumor fraction calculation and is not directly comparable to the F1LCDx results. No other data were excluded from these analyses. Replication was not performed as this is not applicable for clinical data. The statistical methods used to determine study size, as well as randomization and blinding procedures associated with the clinical trial are described in the acelERA primary clinical manuscript. Technical replicates were not performed for ctDNA or tumor RNAseq due to limited sample material. Almost all patients evaluated in this study were female (since this study focused on hormone-receptor positive breast cancer, a disease that primarily affects female patients). These demographics were reported in the primary clinical manuscript. Patient sex was determined by an evaluating physician as part of the clinical trial. Patient gender was not evaluated or collected. Disaggregated sex data is not applicable. Group differences between percent changes or ctDNA levels were calculated using the non-parametric two-sided Mann-Whitney test. Cohorts with normal distributions were evaluated using a two-tailed (two-sided) t-test. Progression-free survival (PFS) was estimated using the Kaplan-Meier method and statistical significance was determined by the two-sided log-rank (Mantel-Cox) test. Hazard ratios (HR) and 95% confidence intervals (CI) were calculated using the log-rank approach. Group comparison and PFS statistical analyses were performed in GraphPad Prism (v10.1.1). Hazard ratios and confidence intervals for exploratory subgroup analysis were estimated using Cox proportional-hazards models using the coxph function (R package survival [v3.8-3]). Visualization as a forest plot was done using the forestplot function (R package forestplot [v3.1.6]) or the forest_model function (R package forestmodel [v0.6.2]). All measurements were taken from distinct samples/patients and were not measured repeatedly. Due to limited sample size and the exploratory nature of this study, and the fact that only a single pre-specified comparison was conducted in these instances, adjustment for multiple hypothesis testing was not performed unless noted otherwise.

Rapid Progression Prediction Model
cv.glmnet() from the glmnet (v4.1-8) R package with family = “binary” and alpha=0.5 were used for the logistic elastic net modeling. pROC was used for AUC. Bone only disease and duration of 2L treatment were dropped due to high levels of missing data. PAM50 and ancestry were dropped due to low N in subgroups influencing results after first confirming the major subgroups had low predictive value. After dropping patients missing data for the remaining clinical features, n = 159 patients remained, with 25 variables.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "I reviewed this manuscript during a previous submission for a different journal. The authors did a good job of addressing my concern and I would recommend publication. The only remaining issue is that Supplemental Fig 5a seems to have incorrect numbers under \"genetic ancestry\". I suspect the columns are swapped for \"TF detected\" and \"TF not detected\", because the sum of the \"TF detected\" category matches the total number of patient with TF *NOT* detected (N=50). I'd ask that the authors please double check all numbers in tables to make sure they are correct.", "reviewer_2": "The authors have adequately addressed my comments.", "reviewer_3": "It is nice to see that the authors have made significant effort to perform thoughtful and rigorous revisions the authors have made to address key points raised in our initial review, but we have remaining concerns that should be addressed given the short turnaround time of their response.\n\nThis manuscript presents a compelling and timely correlative science study in the context of an already reported clinical trial. While the study proposes clinically actionable biomarkers, most notably ER transcriptional activity and ctDNA-based response metrics(C2D1), that have the potential to improve patient outcomes using tools already accessible in many clinical settings, the conclusions may need to be moderated until others are able to replicate their finding or reanalyze the data when publicly available. Nonetheless, the expanded discussion on mechanistic drivers of endocrine sensitivity, real-time ctDNA monitoring, and the feasibility of surrogate assays for ER activity further enhances the translational relevance of this work. This study adds important evidence specific to HR+ breast cancer, demonstrating that early ctDNA clearance and ESR1 MAF reduction (C2D1) can serve as meaningful predictors of response. This complements findings from other cancers (ie. TRACERx, DYNAMIC, and ctMoniTR), where early ctDNA dynamics vary more widely (30–70%) depending on tumor type and treatment modality. The heterogeneity of breast cancer in diverse populations makes this a very important proof of concept paper given the differential overall survival of Black women with HR+ breast cancer but the numbers are small to come to definitive conclusions about predictive value of their finding even though this was a global study. The authors could take time to address the following or discuss why it is not within scope of current study.\n\n1. Tumor ER activity is proposed as a biomarker specific to giredestrant response. That is an overstatement because the survival curves shown in Figure S7b show similar association of ER activity / ER high/low disease with response to giredestrant / PCET. Even though the survival curves in Figure S7b did not reach significant difference, the hazard ratio is actually lower in the PCET subgroups with p = 0.05. A more appropriate way to test interaction of giredestrant treatment with ER high / low would be to fit a single Cox model with treatment received, ER high/low status, and an interaction term - if the interaction term is significant, one could conclude that ER high is specifically a biomarker of response to giredestrant (as stated in the manuscript)- but the data presented do not suggest that ER high is predictive of ONLY giredestrant benefit. Similarly, for ER activity z-score (Figure 5a), a trend towards higher z-score with better response is seen in both treatment groups. A two-way ANOVA or equivalent model should be used to check interaction of response category with treatment received before performing pairwise t-tests between groups - and you should only state that ER activity z score differences are specific to giredestrant if the treatment x response interaction F test is significant.\n\n2. Along those lines, although a formal predictive model for the Rapidly Progressing (RP) subgroup was not developed, the authors provided meaningful exploratory insights and articulated future directions. This is the most clinically significant subgroup! Given the richness of the dataset, a multivariable risk model remains feasible and could offer substantial value in guiding early treatment strategies, especially for Luminal B tumors before they become hormone refractory.\n\n3. Supplemental figure 4 - median PFS values in subgroups would be beneficial to list.\n\n4. For a Pharma led studies, it would be useful if summary date could be deposited into the public domain for secondary analysis if possible."}, "author_response": "Reviewer #1 (Remarks to the Author):\nI reviewed this manuscript during a previous submission for a different journal. The authors did a\ngood job of addressing my concern and I would recommend publication. The only remaining\nissue is that Supplemental Fig 5a seems to have incorrect numbers under \"genetic ancestry\". I\nsuspect the columns are swapped for \"TF detected\" and \"TF not detected\", because the sum of\nthe \"TF detected\" category matches the total number of patient with TF *NOT* detected (N=50).\nI'd ask that the authors please double check all numbers in tables to make sure they are correct.\nThank you for your thoughtful review of our work and for catching this error. We have edited the\ntable and double-checked the other results.\nReviewer #2 (Remarks to the Author):\nThe authors have adequately addressed my comments.\nThank you for taking the time to review our manuscript.\nReviewer #3 (Remarks to the Author):\nIt is nice to see that the authors have made significant effort to perform thoughtful and rigorous\nrevisions the authors have made to address key points raised in our initial review, but we have\nremaining concerns that should be addressed given the short turnaround time of their response.\nThank you for your thoughtful review of the work. The reviews from Nature Cancer were\nreceived on May 6th, and a revision was sent to Nature Communications on July 9th. Revisions\ntook approximately two months, but no additional lab-based experiments were performed.\nThis manuscript presents a compelling and timely correlative science study in the context of an\nalready reported clinical trial. While the study proposes clinically actionable biomarkers, most\nnotably ER transcriptional activity and ctDNA-based response metrics(C2D1), that have the\npotential to improve patient outcomes using tools already accessible in many clinical settings,\nthe conclusions may need to be moderated until others are able to replicate their finding or\nreanalyze the data when publicly available. Nonetheless, the expanded discussion on\nmechanistic drivers of endocrine sensitivity, real-time ctDNA monitoring, and the feasibility of\nsurrogate assays for ER activity further enhances the translational relevance of this work. This\nstudy adds important evidence specific to HR+ breast cancer, demonstrating that early ctDNA\nclearance and ESR1 MAF reduction (C2D1) can serve as meaningful predictors of response. This\ncomplements findings from other cancers (ie. TRACERx, DYNAMIC, and ctMoniTR), where\nearly ctDNA dynamics vary more widely (30–70%) depending on tumor type and treatment\nmodality. The heterogeneity of breast cancer in diverse populations makes this a very important\nproof of concept paper given the differential overall survival of Black women with HR+ breast\ncancer but the numbers are small to come to definitive conclusions about predictive value of\ntheir finding even though this was a global study. The authors could take time to address the\nfollowing or discuss why it is not within scope of current study.\nWe appreciate the reviewer’s thoughtful summary and fully agree that our data, while\nencouraging, remain hypothesis-generating until validated externally (ongoing in larger ph3\nstudies at Roche and elsewhere). We have therefore tempered the language of our key\nconclusions, added explicit caveats, and flagged future validation steps (see manuscript text\nhighlighted throughout in pink).\nThere is a complementary manuscript from our group currently under review at Nature\nCommunications (Liang et al. “Loss of luminal lineage drives resistance to next-generation\nERα-antagonists in pretreated ER + HER2 - locally-advanced or metastatic breast cancer”).\nSimilar to what we see in acelERA, this study demonstrates that giredestrant-responsive tumors\nmaintain high dependency on ER signaling and that non-responsive tumors exhibit loss of\nluminal lineage identity and ER dependence. This manuscript includes follow-up in vitro\nvalidation with giredestrant-resistant cell lines and multi-omic NGS that provide further insight\ninto ER-independence mechanisms. We have suggested to the editorial staff that these\nmanuscripts be co-published, if accepted.\n1. Tumor ER activity is proposed as a biomarker specific to giredestrant response. That is an\noverstatement because the survival curves shown in Figure S7b show similar association of ER\nactivity / ER high/low disease with response to giredestrant / PCET. Even though the survival\ncurves in Figure S7b did not reach significant difference, the hazard ratio is actually lower in the\nPCET subgroups with p = 0.05. A more appropriate way to test interaction of giredestrant\ntreatment with ER high / low would be to fit a single Cox model with treatment received, ER\nhigh/low status, and an interaction term - if the interaction term is significant, one could conclude\nthat ER high is specifically a biomarker of response to giredestrant (as stated in the manuscript)-\nbut the data presented do not suggest that ER high is predictive of ONLY giredestrant benefit.\nSimilarly, for ER activity z-score (Figure 5a), a trend towards higher z-score with better response\nis seen in both treatment groups. A two-way ANOVA or equivalent model should be used to\ncheck interaction of response category with treatment received before performing pairwise\nt-tests between groups - and you should only state that ER activity z score differences are\nspecific to giredestrant if the treatment x response interaction F test is significant.\nThank you for noting the statistical limitations in the previous revision. The Reviewer is correct\nthat PCET and giredestrant hazard ratios for ER high vs. low are similar (0.61 and 0.63).\nMedian PFS with PCET was similar between groups (5.55 vs. 5.14 months), whereas\ngiredestrant caused longer PFS in ER high (7.52 vs. 3.68 months). However, hazard ratios\nsuggest ER high patients may benefit regardless of treatment. We have removed\n“specifically” in the relevant manuscript sections and noted the trends for PCET.\nThe reviewer is correct that both treatment groups show similar directionality (higher ER in\nresponders), but the PCET cohort did not reach statistical significance. The Reviewer’s\nsuggested Cox model and two-way ANOVA revealed that the treatment x response interaction\nwas not significant (p=0.344 and 0.946, respectively). It should be noted that these studies were\nnot powered to test for interaction, and a lack of significance should be interpreted with caution.\nIt should also be noted that the key conclusion still remains – patients with high tumor ER\nactivity receive increased clinical benefit from giredestrant.\nWe have edited the text as such: “ER transcriptional activity…was significantly higher in\ntumors that exhibited stable disease (SD) or PR with giredestrant; PCET showed a similar\ndirection of effect but did not reach significance. A non-significant ER activity × treatment arm\ninteraction (p = 0.946 in Cox model and p=0.344 in two-way ANOVA) suggests ER activity’s\npredictive effect may apply across ETs, though this study was not powered for interaction\nanalysis, an effect not observed with PCET. Patients with high ER tumor activity (z-score >0) or\nmore luminal-like tumors…also showed longer PFS specifically with giredestrant (median 7.5 vs\n3.6 months. The PCET arm showed a similar but non-significant separation (5.6 vs 5.1\nmonths).”\nAs ER activity can diminish with successive therapy lines (Fig. 3d), use of archival biopsies\nmeans some tumors labeled “ER-high” at enrollment could have been ER-low at treatment start,\npotentially diluting the true PFS advantage for ER-high disease. This complicates comparison\nbetween giredestrant and PCET. Restricting to freshly collected tumors (newly added Fig.\nS7f-g) yields a better hazard ratio for giredestrant (0.56 vs. 0.68), but small numbers and\noverlapping confidence intervals warrant caution. We added the following to the results\nsection: “A key caveat is that many tumors were scored from archival biopsies; if ER activity\ndeclined during intervening treatments, some “ER-high” cases were probably mis-classified as\nsuch, dampening the true benefit signal and confounding any direct comparison between\ngiredestrant and PCET.”\n2. Along those lines, although a formal predictive model for the Rapidly Progressing (RP)\nsubgroup was not developed, the authors provided meaningful exploratory insights and\narticulated future directions. This is the most clinically significant subgroup! Given the richness\nof the dataset, a multivariable risk model remains feasible and could offer substantial value in\nguiding early treatment strategies, especially for Luminal B tumors before they become\nhormone refractory.\nWe appreciate the Reviewer’s suggestion and agree this is a worthwhile addition to the\nmanuscript. We developed a formal prediction model for rapid progression using logistic elastic\nnet. Out of 25 variables considered, the final model picked tumor ER activity, prior CDK4/6i\nexposure, presence of visceral mets, and ctDNA detection by F1LCDx. Together the model\npredicted RP with an AUC of 0.79. c-index for each feature considered as well as the model\nROC curve have been added to Figure 5.\n3. Supplemental figure 4 - median PFS values in subgroups would be beneficial to list.\nWe have now added this information to both Supplemental Figures 3 and 4.\n4. For a Pharma led studies, it would be useful if summary date could be deposited into the\npublic domain for secondary analysis if possible.\nThe source data for all figures have been provided as a Source Data file. This file includes\nper-patient tumor ER activity, PAM50, IC10, and luminal scores, all baseline alterations,\nlongitudinal tumor fraction, key baseline demographics, BOR, etc. Any other data supporting the\nfindings of this study may be available from the corresponding author on reasonable request.\nFor eligible studies, qualified researchers may request access to individual patient-level clinical\ndata through a data request platform. For up-to-date details on Roche's Global Policy on the\nSharing of Clinical Information and how to request access to related clinical study documents,\nsee here: https://go.roche.com/data_sharing. Anonymized records for individual patients across\nmore than one data source external to Roche cannot, and should not, be linked due to a\npotential increase in risk of patient reidentification."}, {"round": 2, "reviewer_comments": {"reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_5": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": ""}]

Decision letter