Loss of luminal lineage drives resistance to next-generation ERα antagonists in pretreated ER^+ HER2^− locally-advanced or metastatic breast cancer

Domain: biology
Published: 2026-04-01
Editorial outcome: major_revision

Abstract
Next-generation selective estrogen receptor-α (ERα) antagonist/degraders (SERDs) are being developed for ER-positive breast cancer (ER+ BC), with intentions of improving outcomes for patients. In recent clinical trials of metastatic ER+ BC, next-generation SERDs demonstrated clinical activity, and elacestrant received an approval for advanced ESR1-mutant disease. However, responses to these drugs were highly heterogeneous: across trials and independent of ESR1 status, 30–50% of patients progressed by their first follow-up scan while other patients sustained benefit for 2 years or more. Here, we interrogate the basis for heterogeneous responses by comparing biopsies from non-responding patients (NR; progression-free survival <2 months) and responding patients (Resp; PFS ≥ 2 months) who received the next-generation SERD giredestrant. While Resp tumors maintain high dependency on ERα signaling, NR tumors exhibit loss of luminal lineage identity and by extension, ERα dependence. NR tumors instead up-regulate multiple ERα-independent proliferative pathways, such as EGFR/MAPK and Hippo/TEAD, which may represent targetable dependencies in NR disease. Modeling resistance and lineage plasticity in vitro, we find that giredestrant-resistant ER+ BC cell lines exhibit profound shifts in chromatin accessibility, with the transcription factors, FOXA1 and FOXM1, implicated in gene expression of NR-upregulated proliferative pathways.

Introduction
Breast cancer is the most frequently diagnosed cancer worldwide and a major contributor of mortality1. Approximately 70% of breast cancers belong to the estrogen receptor-positive (ER+) subtype, which is driven by estrogen receptor-α (ERα). Endocrine therapies that inhibit the ERα signaling pathway are the mainstay treatment strategy for this subtype. ER+ breast cancers are commonly treated with aromatase inhibitors (AIs), which block estrogen synthesis. However, prominent use of AIs have selected for ESR1 (encodes ERα) hotspot mutations, whereby mutant ERα adopts a constitutively active conformation to enable estrogen-independent activation2-5. Later lines of therapy include ERα therapeutic ligands such as fulvestrant, the prototypical selective ERα antagonist/degrader (SERD), which binds ERα to antagonize, immobilize and degrade it6. Unfortunately, poor bioavailability7 limits the efficacy of fulvestrant in both ESR1-mutant tumors and other metastatic disease.

Deficiencies associated with existing endocrine therapies inspired widespread development of next generation ERα-targeted therapies8-13, with the intention of improving benefit in all patients including those harboring ESR1 mutations. In the past several years, multiple investigational oral SERDs have been evaluated in clinical trials, including giredestrant9,10,14, elascestrant11, camizestrant13, amcenestrant15, and imlunestrant16. These molecules exhibit on-target activity against the ERα pathway in ER+ breast cancer. Next-generation SERDs showed superior monotherapy activity against ESR1-mutant disease compared to standard-of-care endocrine therapies in the advanced/metastatic setting. However, patients had profoundly heterogeneous responses to these drugs. Across all clinical trials – acelERA17, EMERALD11, SERENA-213, AMEERA-318, and EMBER-319 (NCT04576455, NCT03778931, NCT04214288, NCT04059484, and NCT04975308) – and across experimental and standard-of-care arms, 30-50% of patients had progressed by their first on-treatment scan while others sustained benefit up to 2+ years. These differential responses were observed in both ESR1-mutant cases and the intent-to-treat (ITT) population.

We therefore explored the mechanistic basis for heterogeneous response to SERDs in metastatic ER+ disease by profiling patients with advanced ER+/HER2- breast cancer enrolled in a phase-1a/b study of giredestrant. Here, we show that poor responses to giredestrant were associated with lineage plastic disease and elucidate the molecular underpinnings of lineage-plastic ER+ breast cancer.

Results
Retrospective biomarker analysis on a phase-1a/b giredestrant cohort
We performed exploratory biomarker analyses on biopsies from patients with advanced/metastatic ER+ breast cancer (mBC) enrolled in a phase-1a/b giredestrant dose escalation and expansion clinical trial (GO39932, NCT03332797; Supplementary Fig. 1a) 14. Eligible patients had confirmed ER+/HER2- locally-advanced or metastatic breast cancer, received ≤ 2 prior lines of therapy in this setting, and remained on adjuvant endocrine therapy for ≥ 24 months and/or derived a clinical benefit from endocrine therapy in the advanced/metastatic setting. Patients received giredestrant as a monotherapy (10mg, 30mg, or 90/100mg doses) or in combination with 125mg palbociclib, a CDK4/6 inhibitor (CDK4/6i). Pre-menopausal patients were additionally administered luteinizing hormone-releasing hormone (LHRH) agonists to suppress ovarian function.

Patients provided liquid and tumor FFPE biopsies for retrospective biomarker analyses (Supplementary Fig. 1b). Biopsied patients were generally representative of the study population (Supplementary Fig. 1c). We assessed molecular response to giredestrant via Ki-67 immunohistochemistry (IHC) on n = 29 patients with matching baseline and on-treatment biopsies (C2D8; cycle-2, day-8). Monotherapy giredestrant reduced Ki67 in most cases across dose levels (Supplementary Fig. 1d). All patients who received giredestrant plus palbociclib experienced Ki67 reduction and better responses by RECIST criteria (Supplementary Fig. 1e). Nearly all tumors were either LumA or LumB by PAM50 subtyping (Supplementary Fig. 1f-g). 37% and 31% of patients harbored ESR1 and PIK3CA mutations respectively, as measured by digital droplet PCR (ddPCR) and the comprehensive genomic profiling panel, FoundationACT (F-ACT; Supplementary Fig. 2a-f). However, mutation calls from tumor RNA-seq reads had poor agreement with ddPCR and F-ACT, consistent with known discrepancies between tissue versus liquid-based approaches20 (Supplementary Fig. 2g-h). This cohort’s broader genomic landscape was similar to previous reports in this setting21,22 (Supplementary Fig. 2c).

Benefit on giredestrant is associated with ERα-dependent luminal disease, in both ESR1-mutant and NMD tumors
To interrogate the differences between giredestrant-responsive versus intrinsically-resistant disease, we grouped patients by progression-free survival. Patients who progressed by the time of the first scan (PFS < 2 months) were categorized as non-responders (NR), while those with PFS ≥ 2 months were defined as responders (Resp; Figure 1a). These groups aligned with RECIST responses, with all progressive disease (PD) occurring in NR patients (Figure 1a). We performed univariate Cox proportional hazards modeling on key clinical features – demographics, disease characteristics, and treatment history – to assess their influence on PFS in n = 181 patients (Supplementary Fig. 3a). Only CDK4/6i-related features were associated with PFS (Supplementary Fig. 3a-b). Prior CDK4/6i exposure was associated with significantly worse PFS, while patients enrolled in the palbociclib combination arm had improved PFS, consistent with the prior PALOMA-3 results23. These two effects are difficult to differentiate, since most of the patients in the palbociclib combination arm had no prior CDK4/6i exposure (Supplementary Fig. 3c). ESR1 status was not associated with PFS (Supplementary Fig. 3a-b, d), consistent with other studies in which ESR1-mutant cases treated with next-generation SERDs achieved similar median PFS compared to the ITT population11,13,17. Likewise, ESR1-mutant and NMD (no mutation detected) cases had similar RECIST responses and proportions of NRs versus responders (Supplementary Figure 3e).

To identify the molecular cause of differential response, we unbiasedly compared gene expression between baseline and on-treatment biopsies in responders and non-responders (Figure 1b). Giredestrant did not significantly alter gene expression in NR tumors. By contrast, giredestrant repressed a program of 144 genes in Resp tumors (log -fold change ≤ - 1, FDR < 0.05). These were predominantly comprised of cell cycle genes, such as MKI67, CDC25A, and CCNA2. Unbiased HALLMARK gene set enrichment analysis revealed that suppressed genes were associated with either estrogen response or cell cycle (Figure 1c). Thus, giredestrant inhibits ERα- and proliferation-associated genes in Resp, but not NR tumors.

We explored the basis for differential ERα inhibition by examining key biomarkers. Given the strong influence of CDK4/6i on response (Supplementary Fig. 3a-b), we restricted our initial analyses to patients who received giredestrant monotherapy, paralleling the recent studies in late-line mBC. Among monotherapy-treated patients with baseline biopsies (n = 38), NR cases had a higher median Ki67 %-positivity at baseline (p = 0.06; Figure 1d). Resp patients had significantly higher levels of baseline ERα protein compared to NR (Figure 1d). We assessed giredestrant-ERα engagement by comparing ERα IHC H-scores at baseline and on-treatment for patients with paired biopsies (Supplementary Fig. 4a). Giredestrant reduced ERα to similar extents in NR and Resp tumors, consistent with on-target activity in both groups. Responders also exhibited elevated baseline levels of PgR (progesterone receptor), which was significantly reduced on-treatment (Figure 1d, Supplementary Fig. 4a). Expression of ESR1 and PGR was likewise elevated in Resp patients compared to NR (Supplementary Fig. 4b). Since PGR is an ERα-target gene, we hypothesized that NR and Resp tumors exhibit differential ERα pathway activation.

We examined ERα activity by evaluating established estrogen-induced and estrogen-repressed genes from preclinical models6. Since ERα-induced genes were more impacted by giredestrant exposure (Supplementary Fig. 4c-d), we used this signature to infer ERα activity. At baseline, responders exhibited higher ERα activity than NRs (Figure 1d); this activity was significantly reduced by giredestrant treatment (Supplementary Fig. 4a). By contrast, NRs had low baseline ERα activity comparable to that of giredestrant-treated responders. In a paired baseline and on-treatment analysis, tumors with high baseline ERα activity (categorized by the median value) experienced deep and concomitant suppression of both ERα activity and the E2F proliferation gene signature on-treatment (Supplementary Fig. 4e). In summary, Resp tumors maintain high levels of ERα protein and target gene expression, while NR tumors exhibit reduced ERα protein and low target expression. These data suggest monotherapy Resp cases are highly dependent on ERα and its inhibition via giredestrant drives benefit, while NR cases have lost this dependency and do not respond to giredestrant.

Next, we sought to better understand the heterogeneity in ERα dependence among pretreated ER+ mBC. Loss of hormone receptor dependence has similarly been observed in prostate cancer, in which early disease is driven by androgen receptor (AR). As prostate tumors are exposed to prolonged AR pathway inhibition, a subset of tumors undergo a lineage switch from the AR+ luminal state to the AR- neuroendocrine state and become insensitive to AR-targeted agents24,25. By analogy, early ER+ tumors inherit lineage identity (and thus ERα dependency) from the luminal breast cells. We therefore asked whether differences in ERα activity/dependence could be explained by shifts in the luminal lineage. To address this question, we leveraged our previously-generated breast cancer lineage-specific transcription factor (TF) signatures26. These signatures were defined using TCGA data as the set of TFs respectively enriched in luminal breast cancer (LumA, LumB) versus basal or triple-negative breast cancer. As expected, tumor ERα activity has a strong positive correlation with the luminal TF signature (Figure 1e).

We asked whether the luminal lineage-specific TF signature was associated with response. Indeed, Resp patients had significantly higher luminal TF scores at baseline than NR patients (Figure 1f). Despite the shifts in lineage-specific TF expression, PAM50 subtypes remained similar between luminal-high vs. -low tumors (luminal-high tumors had greater than median ERα activity and luminal TF scores; Figure 1g). Similarly, all tumors retained baseline ERα-positivity of ≥1%. This suggests that although NR tumors have lost some lineage features associated with luminal breast cancer, they have not undergone a wholesale switch of breast cancer subtypes. Finally, we evaluated whether luminal disease was associated with outcome. Independent of study arm, patients with luminal-high tumors had significantly improved PFS compared to luminal-low cases, with median PFSs of 22 months versus 6 months respectively (Figure 1h). Taken together, in the context of giredestrant monotherapy, Resp tumors maintain a strong luminal lineage identity and ERα activity / dependence, whereas NR tumors lose some luminal features and become ERα-independent.

We next assessed patients receiving giredestrant in combination with palbociclib. Since patients receiving the palbociclib combination were predominantly responders (Supplementary Fig. 1e), we compared Resp cases receiving monotherapy (Gired) to those receiving the giredestrant-palbociclib combination (Combo). At baseline, Combo Resp tumors exhibited significantly greater variance in ERα protein levels and luminal TF score compared to monotherapy (Supplementary Fig. 4f). ~70% of Combo Resp tumors exhibited comparable levels as Gired Resp tumors, and the remaining 30% had lower ERα H-scores, ERα activity, and luminal TF score. Likewise, an unbiased gene set enrichment analysis confirms that estrogen response genes were more enriched in Gired Resp tumors (Supplementary Fig. 4g). Conversely, NR-enriched pathways (discussed below) were enriched in Combo tumors (Supplementary Fig. 4f-g). These data suggest that in the context of a CDK4/6i combination, tumors with low ERα dependence (resembling NR tumors in the monotherapy arm) may still experience benefit since CDK4/6 inhibitors target an orthogonal dependency.

Non-responding patients are enriched for putative resistance pathways
We hypothesized that as tumors lose their ERα dependency and luminal identity, they must up-regulate orthogonal signaling pathways to sustain tumor growth. To identify these pathways, we compared luminal-high versus luminal-low tumors by gene set enrichment analysis (Supplementary Fig. 5). As expected, luminal-high tumors were significantly enriched for estrogen-related gene expression. By contrast, luminal-low tumors were significantly enriched for multiple proliferation pathways, including EGFR/MAPK, Hippo/TEAD, Wnt, and others. Across collections, approximately half of all top-enriched gene signatures in luminal-low tumors were associated with immune function (e.g. T-cell and B-cell receptor signaling, interleukin signaling, interferon, PD-1). Of particular interest was the enrichment of inflammatory JAK/STAT, which has been linked to lineage plasticity in prostate cancer27.

We asked if activity in these pathways were associated with response in our cohort. Among monotherapy giredestrant-treated patients, NR patients had significantly higher gene expression scores for MAPK, TEAD, Wnt, and IL2/STAT5 pathways compared to responders (Figure 2a). Of these, MAPK/RTK (receptor tyrosine kinase) signaling and alterations have previously been linked to endocrine resistance22,28,29. Elevation of specific pathways is not easily explained by genomic alterations. While multiple genes associated with orthogonal pathways exhibit increased mutational frequency in NR (e.g. ERBB2), most patients with elevated MAPK signaling did not harbor genomic alterations in that pathway (Figure 2b). These orthogonal pathways may provide a mechanistic basis for ERα-independent disease progression in NR patients.

To further compare non-responders and responders, we performed generalized linear modeling on expression data, with treatment arm and PAM50 as co-variates. This approach identified genes which were significantly associated with either NR or Resp tumors, measured by log -odds ratio (Figure 2c). We identified 1,023 and 1,254 genes associated with NR and Resp tumors respectively. NR-genes included key regulators or downstream targets of MAPK/RTK signaling (e.g. EGFR, MAPK1, MAPK6), TEAD signaling (TEAD4, ANLN, F3), and Wnt signaling (FZD4, FZD5, FZD8), which unbiasedly confirms the elevation of these pathways in NR tumors. Evaluating the expression of these genes, NR and Resp cases exhibited significant elevation of their respective associated genes (Figure 2d). Among responders, both monotherapy and Combo patients had significantly elevated Resp-gene expression and significantly depleted NR-gene expression compared to NR patients. Combo Resp cases had intermediate expression of these genes, with significantly elevated NR-genes and decreased Resp-genes relative to monotherapy Resp tumors (Figure 2d). The gene scores successfully separated NR and Resp tumors, except for a small number of Combo Resp tumors which overlapped with the NRs (Figure 2e).

Since NR tumors lose expression of luminal TFs, we asked what TFs may exhibit increased activity in this context. We performed transcription factor enrichment analysis30 to identify the TFs known to regulate NR- and Resp-genes respectively. As expected, ERα (ESR1) was significantly associated with Resp-genes (Figure 2f). By contrast, NR-genes were significantly associated with transcription factors such as TEAD4 and TCF4, consistent with the observed elevation of Hippo/TEAD and Wnt pathways in NR tumors. Surprisingly, we observed significant enrichment of FOXM1, a transcription factor which is known to both cooperate with ERα to regulate proliferation in ER+ breast cancer cells31,32 and drive ERα-independent proliferation in basal/triple-negative breast cancer33,34 (further discussed below). Taken together, these analyses suggest that while NR tumors exhibit increased transcription factor and pathway activity in multiple proliferative pathways.

Pre-clinical models of giredestrant resistance exhibit loss of luminal identity and gain of NR-associated pathways
To further interrogate NR-associated molecular features, we developed in vitro models of giredestrant resistance in MCF7 and T47D cells. Each model was cultured long-term in 100nM giredestrant and acquired giredestrant resistance over the course of 4 months (GiredR; Figure 3a). As expected, giredestrant immediately reduced ERα protein. Over 4 months of passages, both cell lines further downregulated ERα protein (Figure 3a). While parental cells were sensitive to giredestrant, fulvestrant and tamoxifen (a SERM; selective ERα modulator), GiredR cells exhibited resistance to multiple ERα therapeutic ligands at concentrations of 1µM or more (Figure 3b).

To assess the evolving transcriptome as cells acquire resistance, we compared (1) parental cells, (2) cells at 2 months on-treatment, which retained some ERα protein and had not acquired full resistance, and (3) GiredR cells by bulk RNA-seq. GiredR cells exhibited profound transcriptomic changes compared to the respective parental cells (Supplementary Fig. 6a). For most differentially-expressed genes, changes could already be observed at 2 months (Supplementary Fig. 6b). We asked whether GiredR cells recapitulated the features of NR patients in pretreated ER+ mBC. ERα activity was markedly reduced within 2 months of treatment, consistent with on-target giredestrant activity (Figure 3c) and similar to prior observations in fulvestrant-resistant cell lines35. Luminal TF expression was also reduced in GiredR cells (Figure 3c). However, this reduction occurred later than the acute effect on ERα activity, consistent with prior findings that loss of luminal TFs require prolonged endocrine suppression26. These data also suggest ERα activity and luminal identity can be independently measured; the luminal TF signature remains high at 2 months despite decreased ERα activity. NR-enriched pathways were enriched in GiredR cells, although differences between the two models were observed (Figure 3d). GiredR-MCF7 cells exhibited a profound increase in MAPK pathway activity, and modest increases in TEAD and Wnt activity. By contrast, Gired-T47D cells did not have elevated MAPK activity, but markedly induced TEAD and Wnt activity and upregulated IL2/STAT5 signaling. Unbiased gene set enrichment analysis corroborated these findings (Supplementary Fig. 6c).

We also profiled mutations acquired by GiredR cells. By whole exome sequencing, a limited number of cancer-associated short variants were acquired in MCF7 and T47D GiredR cells. In GiredR-MCF7, variants in FGFR3, HOXD13, and RABEP1 were detected; the FGFR3 variant was V117I, which has no known association with disease. In GiredR-T47D, SETD2, HSP90AA1, KBTBD4, TLE1, and KDM6A were mutated. Likewise, few copy number variations (CNV) were detected. Evaluating CNV between GiredR and parental with log -copy ratio cutoffs of 1 (amplified) or -1 (loss/deletions), no cancer-associated genes were amplified in either GiredR lines. Of the two GiredR cell lines, only MCF7-GiredR had deletions – specifically, 19 cancer-associated genes on chromosome X, of which only UBE2A, XIAP, STAG2, BCORL1, PHF6, RPL10, and ATP6AP1 were expressed in MCF7 cells. Notably, overexpression of ATP6AP1 was previously linked to tamoxifen resistance36, but its deletion here is unlikely to explain resistance in MCF7-GiredR.

As a complimentary approach, we generated MCF7 and T47D cell lines which were resistant to GDC-081037, a therapeutic ligand exhibiting pharmacological features of both SERDs and SERMs (SERD-SERM hybrid). GDC-0810 resistant cells (0810R) were established by culturing parental cells with increasing concentrations of GDC-0810 over 6 months (Supplementary Fig. 7a). Two clones per cell line were analyzed. As with GiredR cells, the 0810R cells were cross-resistant to fulvestrant and tamoxifen at concentrations of 1µM or more. Luminal TFs were repressed across 0810R clones (Supplementary Fig. 7b). In both 0810R models, transcriptional profiling revealed the loss of ERα activity and enrichment of orthogonal pathways (Supplementary Fig. 7c-d), and proteomic profiling via reverse phase protein array (RPPA) revealed decreased ERα protein (Supplementary Fig. 7e). Protein levels of EGFR and MAPK pathway components were increased in 0810R-MCF7, but not in 0810R-T47D cells. Together, these data demonstrate that the same resistance-associated features emerge across two different classes of anti-ERα therapeutic ligands: full SERDs (giredestrant) and SERD-SERM hybrids (GDC-0810).

ERα inhibition promotes increased chromatin accessibility at ERα-independent sites associated with FOXA1 and FOXM1
We asked whether epigenetic alterations may promote ERα independence upon prolonged ERα inhibition. We performed bulk ATAC sequencing to evaluate the chromatin accessibility landscape in parental cells, 2 month-treated cells, and GiredR cells. For both models, GiredR cells exhibited profound shifts in chromatin accessibility compared to their parental counterparts (Figure 4a). GiredR cells had altered accessibility at 47,380 and 22,446 sites in MCF7 and T47D respectively (34% and 17% shifts in accessibility). In both models, the number of chromatin sites gained in GiredR cells were 4 to 6-fold higher than the number of sites lost. Loss of GiredR-repressed sites (down in GiredR) occurred prior to the elevation of GiredR-induced sites (up in GiredR; Figure 4b-c). Combined analyses across models yielded similar results (Supplementary Fig. 8a-c). To probe the functional importance of GiredR-repressed and induced chromatin sites, we performed motif enrichment analysis on each. The ERα (ESR1) motif was significantly enriched among repressed sites (Figure 4d), indicating that ERα binding sites become inaccessible in GiredR cells. By contrast, GiredR-induced sites were enriched for multiple Forkhead (FOX)-family members and TEAD (Figure 4d, Supplementary Fig. 8d). Across induced sites, the top enriched motifs were FOXM1 and FOXA1 (Figure 4d); although other FOX-family motifs were enriched, these factors are not expressed in either cell line. The enrichment of FOXM1-associated chromatin sites parallels the association between NR-specific genes and FOXM1 transcriptional activity in NR patients (Figure 2f). However, the enrichment of FOXA1 sites was surprising given its role as a master regulator of the luminal lineage in both healthy breast and ER+ BC.

FOXA1 normally acts as a pioneer factor for ERα, enabling accessibility at key ERα-target and luminal genes38,39. We assessed the co-occurrence of ESR1 and FOXA1 motifs among GiredR-repressed and induced sites. Evaluating all sites containing ERα and/or FOXA1 motifs, 70% of GiredR-repressed sites contained an ERα motif without a co-occurring FOXA1 motif (Supplementary Fig. 8e). Conversely, 70% of GiredR-induced sites are represented by FOXA1 alone. A similar pattern is observed when comparing ERα to FOXM1 motifs among differential GiredR sites, with FOXM1 motifs occurring distinctly from FOXA1 in half of relevant sites (Supplementary Fig. 8e-f). We characterized ERα-independent FOXA1 sites in GiredR cells. Compared to parental FOXA1 sites, GiredR FOXA1 sites were enriched for motifs of MAPK-associated factors (MEF2B, MEF2D), TEAD factors (TEAD1, TEAD4) and STATs (STAT5B, STAT4; Figure 4e). Some model-specific motifs were observed, such as the enrichment of AR (ANDR) motifs in T47D (Supplementary Fig. 8g). Sites containing FOXM1 motifs were likewise enriched for these motifs (Figure 4e, Supplementary Fig. 8g).

To confirm differential FOXA1 and FOXM1 binding in parental versus GiredR cells, we performed ChIP-seq (Supplementary Fig. 9a). For FOXA1, 135,218 and 144,854 ChIP sites were captured in MCF7 and T47D respectively, with 54% of sites containing the FOXA1 motif. FOXA1 exhibited differential chromatin binding at 40-50,000 sites (Figure. 4f-g, Supplementary Fig. 9b), approximately half of which were shared between models (Supplementary Fig. 9c). In both GiredR models, FOXA1 was bound to the promoters of NR pathway genes, such as MAPK (MAP4K3, KRAS, DUSP10), TEAD (YAP1, ANLN, TAGLN) and Wnt (TCF4, CTNNB1, FZD4). Motif enrichment analyses of GiredR-enriched FOXA1 ChIP sites identified an enrichment of related motifs, such as TEAD1 and TEAD4 (Supplementary Fig. 9d). For FOXM1, 12,724 and 13,309 ChIP sites were respectively captured, consistent with a previous report40; 23% contained FOXM1 motifs. In GiredR cells, FOXM1 had elevated binding at 823 and 3,222 sites in the respective models (Supplementary Fig. 9e-g). Although analysis identified parental-enriched FOXM1 sites, signal at these sites was weak and likely not meaningful. FOXM1 was bound to a higher frequency of promoter regions compared to FOXA1 (Supplementary Fig. 9h), was bound to promoters of key genes such as YAP1, and had enrichment of NR pathway-associated motifs such as TEAD1/4 (Supplementary Fig. 9i).

To test the functional importance of GiredR-induced chromatin sites, we identified the genes within 3kb of chromatin sites in which FOXA1/FOXM1 motifs co-occurred with MEF2, TEAD, and STAT motifs (Supplementary Fig. 10a). Evaluating expression of these genes over treatment, we observed significant transcriptional induction in 2 month-treated and GiredR cells (Figure 4h, Supplementary Fig. 10b). Compared to parental cells, genes proximal to FOXA1+MEF2 sites were significantly elevated at 2 months and further elevated in GiredR (Figure 4h). Likewise, genes proximal to FOXA1+TEAD1/4 sites and FOXA1+STAT4/5B sites were significantly elevated on-treatment. Similar expression trends were observed for genes near sites where FOXM1 co-occurred with these motifs (Supplementary Fig. 10b). Together, the data suggest that FOXA1/FOXM1 act at ERα-independent chromatin sites, which induces gene expression in pathways associated with NR disease.

NR-associated pathways may be targetable in the context of giredestrant resistance
We sought to identify therapeutic vulnerabilities in the context of giredestrant resistance. Despite FOXM1’s involvement in giredestrant resistance, GiredR cells were insensitive to the siRNA-mediated knockdown of FOXM1 (Supplementary Fig. 10c-d). We next considered the MAPK and TEAD pathways, given their upregulation in both NR patients and GiredR models. To broadly assess whether these pathways were associated with giredestrant resistance across pre-clinical models, we leveraged relative growth rates in giredestrant for 14 ER+/HER2- breast cancer cell lines using the GR method41,42. A relative growth rate of 1.0 denotes complete resistance (the cell line grows as well in giredestrant as in the absence of drug), whereas a relative growth rate of 0.0 denotes complete growth inhibition in the presence of giredestrant. We examined the association between growth rate in giredestrant and ERα, MAPK, and TEAD activity across the 14 cell line models. ERα activity exhibits a strong inverse correlation with the models’ ability to grow in the presence of giredestrant, likely because it reflects each model’s degree of ERα dependence (Supplementary Fig. 10e). Conversely, both MAPK and TEAD activities across cell lines were positively correlated with their ability to grow in the presence of giredestrant. The MAPK observations are consistent with a previous report in which MCF7 cells acquired fulvestrant resistance upon lentiviral-transduced EGFR overexpression22. Both MAPK and TEAD activity were inversely correlated with ERα activity across the 14 models (Supplementary Fig. 10f).

We tested whether either pathway would be targetable in the context of GiredR cell lines. We treated parental and GiredR cells with the clinically-approved MEK inhibitor, cobimetinib, and the investigational TEAD inhibitor, GNE-788343. GiredR-MCF7 cells exhibited profound sensitivity to both inhibitors, with IC50 of ~100nM and 10nM respectively (Figure 4i). 0810R-MCF7 clones were also sensitive to cobimetinib (Supplementary Fig. 7f). By contrast, parental MCF7 cells were insensitive to TEAD inhibition and showed some sensitivity to cobimetinib at higher concentrations (Figure 4i). GiredR-T47D cells exhibited weaker responses. GiredR-T47D cells had increased sensitivity to cobimetinib and GNE-7883 compared to parental, but its IC50 for cobimetinib was ~1µM and IC50 for GNE-7883 was not reached. Similarly, the cobimetinib IC50 in 0810R cells was ~5µM for one of the two clones (Supplementary Fig. 7f). Thus, giredestrant-resistant cells have increased sensitivity to MAPK and TEAD inhibition, although the degree varies by model.

Methods
Our research complies with all relevant ethical regulations. Study GO39932 (NCT03332797) was conducted in accordance with the Declaration of Helsinki and Council for International Organizations of Medical Sciences International Ethical Guidelines, the International Council for Harmonization Good Clinical Practice guidelines, or applicable laws and regulations of each country where the research was conducted if they provided greater protection to the individual. Study GO39932 exclusively enrolled female patients; all patients provided written informed consent and were not compensated for their participation. The protocol, protocol amendments, informed consent form, Investigator Brochure, and other relevant documents (for example, advertisements) were submitted to an Institutional Review Board (IRB)/Independent Ethics Committee (IEC) by the investigator and reviewed and approved by the IRB/IEC before the study was initiated. The study was conducted in 23 clinical sites and each site reviewed the protocol via its own institution's ethics committee and criteria; see Supplementary Table 1 for full list of institutions and IRBs. Any amendments to the protocol required IRB/IEC approval before implementation of changes made to the study design, except for changes necessary to eliminate an immediate hazard to study patients. Patients did not receive intervening treatment prior to collection of baseline samples. All samples were tested in accordance with patient consent.

Histology & immunohistochemistry
H&E and immunohistochemistry was completed by HistoGeneX (now CellCarta, www.cellcarta.com). Estrogen receptor: SP1 on Ventana Benchmark ULTRA according to package insert. Progesterone receptor: 1E2 on Ventana Benchmark ULTRA according to package insert. KI67: VENTANA CONFIRM kit (30-9) on Ventana Benchmark ULTRA according to package insert. Definiens digital image analysis was scored as a continuous percentage of positively-stained tumor cells.

H-score calculation for tumor immunohistochemistry
Immunohistochemistry-labeled slides are reviewed by a trained pathologist. For the marker of interest, staining intensity is categorized into four possible buckets: 0 (negative), 1+ (weak), 2+ (intermediate), and 3+ (strong). H-score is calculated using the following formula: H-score = 1 x (% of intensity 1+ cells) + 2 x (% of 2+ cells) + 3 x (% of 3+ cells). A maximum score of 300 can be achieved, which reflects 100% of the tissue section having 3+ intensity for the marker of interest.

RNA sequencing
For FFPE tumor biopsies, the H&E slide stained at HistoGeneX (now CellCarta) was used as a guiding slide for microdissection. After microdissection of the tumor area, RNA was extracted from 4-5 slides of 5 microns. An elution volume of 50 μL was used and total RNA was quantified with RiboGreen. RNA-seq was performed by Q2 Solutions - EA Genomics using the TruSeq RNA Access method (www.q2labsolutions.com/en/genomics-laboratories). Total RNA library preparation and coding RNA library enrichment was followed by paired-end sequencing (2 x 50b, 50M reads) using the Illumina sequencing-by-synthesis platform.

For cell line experiments, total RNA was extracted from cells using Qiagen’s AllPrep kit as per manufacturer’s protocol. Quality control of samples was done to determine RNA quantity and quality prior to their processing by RNA-seq. The concentration and the integrity of total RNA samples was determined using a 2200 TapeStation instrument (Agilent Technologies). 1ug of total RNA was used as an input material for library preparation using TruSeq RNA Sample Preparation Kit v2 (Illumina). Library size was confirmed using the 2200 TapeStation and High Sensitivity D1K screen tape (Agilent Technologies) and their concentration was determined by qPCR based method using Library quantification kit (KAPA). The libraries were multiplexed and then sequenced on HiSeq2500 (Illumina) to generate 30M of single end 50 base pair reads.

Digital droplet PCR
Digital droplet PCR (Inostic BEAMing) was performed by Sysmex Inostics; see https://sysmex-inostics.com for full methods. Cell-free DNA was isolated from liquid biopsies using the QIAamp DSP Circulating Nucleic Acid purification kit (Qiagen). Genomic DNA from plasma samples was quantified by LINE-1 quantitative real-time PCR and used as a template for pre-amplification under high-fidelity PCR conditions. The pre-amplified DNA is used for the subsequent BEAMing assay. Pre-amplified DNA fragments are amplified by emulsion PCR on the surface of magnetic beads in water in oil-emulsions, then hybridized to fluorescent probes and quantified by flow cytometry. The result is the fraction of mutant DNA alleles to wild-type DNA alleles present in a particular sample, calculated by dividing the amount of mutant beads by the total amount of beads with PCR product. ESR1, PIK3CA, and AKT1 mutations were evaluated for each sample. Specific variants screened: ESR1 – E380Q, S463P, V534E, P535H, L536H, L536P, L536R, L536Q, Y537N, Y537S, Y537C, and D538G; PIK3CA – C420R, E542K, E545K, E545G, Q546K, M1043I, H1047Y, H1047R, and H1047L; and AKT1 – E17K.

Genomic profiling by liquid biopsy
Genomic profiling (GP) of plasma samples was performed with the FoundationACT assay in a Clinical Laboratory Improvement Amendments (CLIA)-certified, College of American Pathologists (CAP)-accredited reference laboratory (Foundation Medicine, www.foundationmedicine.com). FoundationACT uses hybrid-capture, adapter ligation-based libraries to identify genomic alterations (base substitutions, small insertions and deletions, copy number alterations, and select rearrangements/fusion events) in 66 cancer-related genes47. Of the plasma samples tested, 85 samples collected at baseline and 38 samples collected at EOT were successfully sequenced, having passed quality control parameters for DNA concentrations, library preparation, tumor purity, and target region coverage. Of these, 28 cases were paired, representing patients with successful sequencing of both baseline and EOT plasma samples.

Cell culture
MCF7 and T47D cells cultured at 37°C and 5% CO , in RPMI-1640 media supplemented with 10% FBS and 2 mM L-glutamine. Cell line authentication was conducted by Genentech’s centralized cell repository. Short tandem repeat (STR) profiles were determined for each line using the Promega PowerPlex 16 System. This is performed once and compared to external STR profiles of cell lines (when available) to determine cell line ancestry. SNP profiles were performed each time new stocks were expanded for cryopreservation. Cell line identity was verified by high-throughput SNP profiling using Fluidigm multiplexed assays. SNPs were selected based on minor allele frequency and presence on commercial genotyping platforms. SNP profiles were compared with SNP calls from available internal and external data (when available) to determine or confirm ancestry.

Generation of drug-resistant cell line models
GiredR: To establish GiredR cells, parental MCF7 and T47D cells were treated with 100nM giredestrant (prepared in DMSO) for 4 months. GiredR cells were maintained in culturing media containing 100nM giredestrant. Prior to experiments, giredestrant is removed from culturing media for 48 hours.

0810R: To establish 0810R cells, parental MCF7 and T47D cells were treated with 0.1µM GDC-0810 (prepared in DMSO). The concentration of GDC-0810 was increased stepwise over the course of 6 months to a final concentration of 8µM. Individual clones were subsequently isolated by serial dilution or FACS. 0810R clones were maintained in culturing media containing 8µM GDC-0810.

Cell viability
Cell viability is evaluated using the CellTiterGlo assay (Promega, G7570), performed per manufacturer’s protocol using an EnVision plate reader (PerkinElmer, 2104-0010A). Resistant clones are re-plated and maintained in the absence of drug for 24hr. Then, the respective drugs are added to culture at specified concentrations, and viability measurement is taken after 7 days.

Protein analysis for cell lines
Protein extracts were prepared with RIPA Lysis Buffer (EMD Millipore). For immunoblotting, resistant cells are re-plated, maintained in the absence of giredestrant/GDC-0810 for 24hr, treated for 24hr, and then harvested. The following antibodies were used: ERα (ThermoFisher, clone SP1, MA5-14501), FOXM1 (Cell Signaling, clone D3F2B, 20459), and β-actin (Cell Signaling, clone 8H10D10, 3700). Reverse phase protein array (RPPA) was conducted by Theranostics Health for MCF7 parental and SR clones 1 and 2. Protein lysates from each cell line were diluted to a final concentration of 0.5 mg/mL, and samples were printed in duplicate on slides. The intensity value for each end point was determined within the linear dynamic range of staining after background subtraction (local background and slide staining with secondary antibody only). Intensity values were used to compute log -fold change values per protein species across experimental conditions.

FOXM1 siRNA
Per reaction, approximately 2 million cells were transfected with 30 pmol of either non-targeting control or FOXM1 siRNA. Cells were transfected using the Amaxa cell line nucleofector kit V (Lonza, VCA-1003) and electroporated using Nucleofector program P-020 per manufacturer’s protocol. Cells were seeded at a density of 0.1 million/well in a 6-well plate format, then allowed to recover for 24 hours. Following transfection, cells were grown in an IncucyteT instrument to enable monitoring of cell growth rates via phase contrast, with imaging every 4 hours using a 10X objective. Cells were collected at the end of the 6-day monitoring period to confirm FOXM1 knockdown via Western blot analysis.

siRNA oligonucleotides were synthesized by Horizon Biosciences; control siRNA was a pool of four non-targeting guides: UGGUUUACAUGUCGACUAA, UGGUUUACAUGUUGUGUGA, UGGUUUACAUGUUUUCUGA, and UGGUUUACAUGUUUUCCUA; FOXM1 siRNA: CCAACAAUGCUAAUAUUCA, CAUUGGACCAGGUGUUUAA, GCGCACGGCGGAAGAUGAA, and UGAAAGACAUCUAUACGUG.

Whole exome sequencing
Genomic DNA was quantified with Qubit dsDNA HS Assay Kit (ThermoFisher, Q32851) and quality was assessed using Genomic DNA ScreenTape on 4200 TapeStation (Agilent, 5067-5582). For sequencing library generation, the SureSelectXT HS2 DNA System (Agilent) was used with an input of 200 ng of genomic DNA. Libraries were quantified with Qubit dsDNA HS Assay Kit and the average library size was determined using D1000 ScreenTape on 4200 TapeStation. 750 ng of the library is hybridized to capture probes from the SureSelect Human All Exon V7 kit (Agilent, 5191-4004). Captured libraries were quantified with Qubit dsDNA HS Assay Kit and the average library size was determined using D1000 ScreenTape on 4200 TapeStation. Captured libraries were pooled and sequenced on NovaSeq 6000 (Illumina) to generate 50 millions paired end 75-base pair reads for each sample.

ATAC sequencing
Nuclei were isolated from ~25,000 cells per population, sorted by FACS. For each sample, the transposition reaction was performed in a 50μL volume using 2.5μL Nextera Tagment DNA enzyme (Illumina, 15027865) and 25μL of Nextera Tagment DNA buffer (Illumina, 15027865). Reaction was incubated in a 37°C thermomixer at 1000rpm for 30 minutes, then terminated by adding 5μL of stop buffer (5M NaCl, 1M EDTA). DNA was purified and eluted in 15μL volume using the MinElute Reaction Cleanup kit (Qiagen, 28204). Transposed DNA was stored at -80°C for up to two weeks prior to library preparation.

Partially amplified libraries were generated by amplifying the purified transposed DNA for 5 cycles using NEBNext High-Fidelity 2X PCR Master Mix (New England Biolabs, M0541L). Partially amplified libraries were quantified by qPCR to determine the cycle number at which each sample produced 25% of maximum fluorescent intensity. The fluorescent intensity was used to estimate the number of additional PCR cycles required for each sample. Fully amplified libraries were purified using the SPRIselect beads (Beckman Coulter, B23317). Purified final libraries were quantified using the Qubit dsDNA HS Assay Kit (ThermoFisher, Q32851) and profiled using the Bioanalyzer High Sensitivity DNA Kit (Agilent Technologies). Libraries were pooled and sequenced on a HiSeq 2500 or NovaSeq 6000 instrument (Illumina) to generate 30 million paired-end 50-base pair reads per library.

Chromatin immunoprecipitation (ChIP) sequencing
Per biological replicate, approximately 3 million cells were used as input for ChIP-seq. Cells were double crosslinked with 50mM disuccinimidyl glutarate (ProteoChem, C1104) for 30 minutes, followed by 10-minutes of 1% formaldehyde. Formaldehyde was quenched by the addition of glycine. Nuclei were isolated with ChIP lysis buffer (1% Triton x-100, 0.1% SDS, 150 mM NaCl, 1mM EDTA, and 20 mM Tris, pH 8.0). Nuclei were sheared with a Covaris sonicator using the following setup: fill level: 10, duty cycle: 15, PIP: 350, cycles/burst: 200, and time: 8 minutes. Sheared chromatin was immunoprecipitated overnight with the respective antibodies. Antibody-chromatin complexes were pulled down with Protein A magnetic beads and washed twice in IP wash buffer I (1% Triton, 0.1% SDS, 150 mM NaCl, 1 mM EDTA, 20 mM Tris, pH 8.0, and 0.1% NaDOC), twice in IP wash buffer II. (1% Triton, 0.1% SDS, 500 mM NaCl, 1 mM EDTA, 20 mM Tris, pH 8.0, and 0.1% NaDOC), twice in IP wash buffer III (0.25 M LiCl, 0.5% NP-40, 1mM EDTA, 20 mM Tris, pH 8.0, 0.5% NaDOC), and once in TE buffer (10 mM EDTA and 200 mM Tris, pH 8.0). DNA was eluted from the beads by vigorous shaking for 20-minutes in elution buffer (100mM NaHCO3, 1% SDS). DNA was de-crosslinked overnight at 65C and purified with MinElute PCR purification kit (Qiagen, 28004). DNA was quantified by Qubit, and 2ng of DNA was used for sequencing library construction with the Ovation Ultralow Library System V2 (Tecan, 0344NB-A01) using 15 PCR cycles according to the manufacturer’s recommendations. Libraries were sequenced with Illumina Nextseq, using paired-end 50-bp read configuration. Antibody clones used for ChIP-seq: anti-FOXA1 (Abcam, clone EPR10881, ab170933) and anti-FOXM1 (GeneTex, GTX102170).

Bioinformatics data analysis
RNA sequencing
Processing reads: Raw reads were processed using the HTSeqGenie workflow. Briefly, reads with low nucleotide qualities (70% of bases with quality <23) or matches to rRNA and adapter sequences were removed. The remaining reads were aligned to the human reference genome, GRCh38.p10, using GSNAP48,49 (v.2013-10-10-v2), allowing maximum of two mismatches per 75 base sequence. Transcript annotation was based on the Gencode human database, GENCODE 27. To quantify gene expression levels, the number of reads mapping unambiguously to the exons of each gene was calculated.

Analysis: Limma-voom in R was used for differential gene expression analysis. For patient data, z-scores per patient were computed from voom-normalized gene expression values, and further normalized to a reference cohort of 138 ER+ breast cancer cases. Cell line data were not normalized to the reference breast cancer cohort. For each pairwise comparison between conditions, log fold change per gene was calculated. Gene set enrichment analysis was performed using the ‘fgsea’ method with the Broad Institute’s MSigDB gene signatures50-52. Heatmaps were plotted with the ComplexHeatmap package. Transcription factor enrichment analysis was performed using the ChEA3 platform30.

Mutation inference: Tumor mutational status was inferred from each RNA-seq sample by detecting the presence of mutant alleles in the respective BAM files. Samples were categorized as mutant if at least 5% of the corresponding nucleotides encoded the mutant allele. ESR1 mutational status was assessed using RNA-seq reads of the ESR1 gene, for the following clinical variants: E380Q, S463P, V534E, P535H, L536H/P/R/Q, Y537N/S/C, and D538G. PIK3CA mutational status was assessed using RNA-seq reads of the PIK3CA gene, for the following clinical variants: C420R, E542K, E545K/G, Q546K, M1043I, and H1047Y/R/L.

Gene signature scores: Gene signature scores were computed as the average of z-scores for all genes defined in each signature. Gene signatures were defined in previous studies – ERα-induced genes6: OLFM1, NXPH3, AMZ1, CELSR2, CT62, RBM24, FKBP4, SGK3, PPM1J, FMN1, IGFBP4, AREG, RAPGEFL1, PGR, RET, TFF1, ZNF703, RERG, SLC9A3R1, GREB1, and NOS1AP; ERα-repressed genes6: STON1, EGLN3, FAM171B, LIPH, SSPO, BAMBI, NBEA, GRM4, PNPLA7, DDIT4, TP53INP2, TGFB3, PSCA, BCAS1, CCNG2, TP53INP1, and SEMA3E; luminal TF genes26: XBP1, ESR1, FOXA1, ZNF552, PGR, RARA, GATA3, CXXC5, AR, DACH1, ZNF703, ZNF467, SPDEF, ZBTB42, and CREB3L4; MAPK target genes53: SPRY2, SPRY4, ETV4, ETV5, DUSP4, DUSP6, CCND1, EPHA2, and EPHA4.

Genomic profiling
Processing reads: Processed mutational calls generated by FoundationMedicine; read processing, variant calling and filtering, and copy number alteration calling were performed as previously described47. Per sample, each gene in the assay panel was categorized as either non-mutant or mutant. Specific alleles and alteration type (e.g short variant, amplification/deletion) were provided for mutation calls.

Whole exome sequencing
Processing reads: Raw reads were processed using the GATK4 Best Practices workflow for whole exome somatic variant discovery and annotation, on human genome (GRCh38). BAM files resulting from the pipeline were used to determine copy number variation for GiredR samples using respective parental sample as the reference. Variant calling was performed on GiredR cells relative to a parental cell reference sample, using Mutect2 from GATK (v4.1.4.1). Mutational calls were processed per sample using base R v4.1.0 tools. Copy number variation calls were assessed using the CNVkit pipeline54.

Analysis: Mutations were considered cancer-associated if the corresponding gene is included in the OncoKB cancer gene list55. For copy number variation, a gene was considered amplified if the log -copy number ratio between GiredR and parental was ≥ 1 and loss if the ratio was ≤ - 1.

ATAC- and ChIP sequencing
Processing reads: Raw reads were processed through the ENCODE ATAC-seq or ENCODE ChIP-seq pipelines respectively, using MACS2 criteria for peak calling56,57 (statistical cutoff: p-value > 1x10-6). Reads were mapped to human genome GRCh38. Analysis: To perform differential peak analysis, the DiffBind package was used to calculate log -fold change and FDR values for each peak between two conditions. Heatmaps and average peak profiles were computed and plotted using the profileplyr package. For motif analyses, the MEME suite58 (5.3.3) tools were used – (1) ‘analysis of motif enrichment’ (AME) with the human HOCOMOCO v11 FULL motif database59 to identify motifs within peaks of interest and (2) ‘find individual motif occurrences’ (FIMO) to map motifs to individual peaks. Due to input size limits, a maximum of 60,000 peaks were evaluated in each AME/FIMO analysis; if a set of peaks exceeded this limit, 60,000 peaks were randomly-sampled to use as input for analysis. AME enrichment scores (‘% enrichment over reference’) were calculated from AME output as: %TP (true positive) - %FP (false positive). To map peaks to genomic features and genes associated with peaks, the chipenrich package was used.

Statistical analysis
Statistical analyses of experimental data were performed in R (v. 4.3.1) or Graphpad Prism 10. P-values are reported in respective figure panels and statistical tests are reported in respective figure captions.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "I find this manuscript compelling for understanding the response and resistance mechanisms associated with SERDs, SERMs, CDK4/6 inhibitors, and the combination of SERD and CDK4/6 inhibitors. The laboratory work and analysis of clinical samples complement each other, creating a strong synergy in the research.\nAll of the reviewer's comments have been addressed accurately and systematically. Many new experiments and analyses have been incorporated, leading to valuable research findings that have been effectively presented.\nIn Figure 3, the response to Tamoxifen (TAM) appears bell-shaped. From a target concentration perspective, it seems that certain regulatory mechanisms may be involved.", "reviewer_4": "Through the revisions, Liang et al appropriately reframed analyses to account for the difference in prior CDK4/6 inhibition, more clearly demonstrating the differences in responders versus non-responders to giredestrant. Their expanded in vitro analysis across resistant cell lines adequately showed common pattern of loss of ER signaling and an up-regulation of alternative pathways, EGFR/MAPK and Hippo/TEAD. The additional ATAC-seq data exploring a mechanistic underpinnings for endocrine resistance and loss of luminal features. Demonstrating the limitations of mutation calls over ddPCR are clinically important and should be highlighted. Further delineating those patients who had prior CDK4/6i treatment vs those who are treatment naive will continue to improve the applicability of the study population to real-world patients, most of whom will have received CDK4/6 therapy. I have a few additional minor comments below.\nMinor comments:\nLine 201, pg6: while definitions of luminal-high and luminal-low is clearly written in the methods, it would benefit the reader to add a brief definition within the main body of the text rather than the figure legend.\nFigure 4e: I could not follow what the two different barplots refer to. What is the difference between the left and right parts of 4e? They look identical with no indicating title or description in the figure legend.\nExtended Data Figure 2: is it possible to add prior CDK4/6 treatment as a color bar under response? This could aid in visualization and interpretation of the figure.\nExtended Data Figure 3b: for the panel demonstrating Treatment arm, is it possible to also demonstrate the survival curve within the Combo group of those who had received prior CDK4/6 inhibition?", "reviewer_5": "In the manuscript “Loss of luminal lineage drives resistance to next-generation ERa-antagonists in pretreated ER+ HER2- locally-advanced or metastatic breast cancer”, Liang et al. investigate the mechanistic basis for response to treatment in the phase 1a/b study with the oral selective estrogen receptor degrader (SERD) giredestrant (NCT03332797) alone or in combination with a CDK4/6 inhibitor. The authors have performed exploratory analyses of both tumor biopsies and ctDNA from patients on the trial and in addition have developed in vitro cell line models of giredestrant resistance. In the trial patients with metastatic ER+/HER2- breast cancer were treated with giredestrant at several different dose levels and a subset were treated with the combination of giredestrant plus palbociclib. Given the nature of the trial and the lack of a control arm, the results are largely hypothesis generating. Nonetheless the data from the trial are valuable and should be reported.\nMajor critiques:\n1) While the primary data from the in vitro models has been deposited in GEO, the RNA-seq and liquid biopsy data from the trial should also be made available.\n2) The authors argue that in the giredestrant resistant models that the inability to identify a clear genetic mechanism of resistance suggests that resistance is primarily epigenetically driven. The data presented do not support this conclusion. The stable resistance phenotype of GirdR models may indicate genetic selection and there may be different genetic mechanisms in MCF7 or T47D cells. In addition, there may be multiple different genetic mechanisms in distinct clones present in the GirdR cultures that may mask distinct genetic resistance mechanisms. Single cell cloning or sequencing would be needed to address this possibility. In Figure 3e, using whole exome sequencing (WES), over 50 mutations are enriched in GirdR cells, including known cancer-associated variants, but follow-up experiments are not performed. Additionally, copy number variation (CNV) analysis was performed on GirdR cells, which identified a loss on chromosome X including 19 cancer associated genes (Line 335), but this data is not presented in the figure and there is no validation. Deletions found in the CNV analysis further supports genetic selection of a SERD resistant clone. The authors state “Since few genetic mutations were acquired by GiredR cells, we considered whether epigenetic alterations may drive ERα independence and loss of luminal identity” (Line 362), and in Figure 5 they propose an epigenetic model of endocrine resistance. The authors should point out more clearly that genetic mechanisms of resistance remain a likely explanation despite their inability to identify them.\n3) This manuscript relies heavily on computational imputation without validation. For example, in Figure 4d-f, ATACseq identified chromatin opening at FOXM1 motifs, which the authors argue is a novel driver of endocrine resistance (Line 34), however the ATACseq results do not necessarily demonstrate a functional role for FOXM1. For example, the FOXM1 motif highly resembles the FOXA1 motif, a known driver of endocrine resistance. A direct role for FOXM1 should be validated using FOXM1 ChIPseq and testing whether FOXM1 is necessary for resistance in GirdR cells and sufficient to induce resistance in parental cells.\n4) The use of the FOXM1 inhibitor FDI-6 is not convincing. What is the evidence that its effects are on-target? Can the effects be reproduced genetically?\n5) The model presented in Figure 5 is not supported by the data presented.\nOther questions and minor concerns:\n1) Are any ESR1 gene amplifications, deletions, or gene fusions identified in patients?\n2) The methods section indicates that WES was performed using a mouse genome kit (Line 1045).\n3) Please define NMD (Line 128) and specify the genes for the Luminal TF (Figure 1F) and the MAPK signature (Ext Data Fig 9a) in this manuscript, and update Ref #26.", "reviewer_1_attached": "1) This study deals with an essential topic in the treatment of recurrent breast cancer, especially HR+/HER2-. It is a prospective translational study, and its significance is considered high. On the other hand, the number of cases is limited, and the number of analyses of tissue-paired samples is relatively small, so its interpretation should be done with caution.\n2) On the other hand, there are countless previous reports in this research field and a large amount of related literature and data. The consistency and inconsistency with these would be the essential evaluation points of this study.\n3) The concept of treatment resistance is vague in the introduction and discussion. There is endocrine therapy (ET) resistance, in which case there is complete resistance to ET, which might be related to FP in this study), resistance about each class and intraclass resistance. This study addresses class-related resistance in interpreting the analysis results of human samples and cell line experiments. Intraclass resistance may correspond to the relationship with fulvestrant resistance and the comparison of resistance between new SERDs. It may be difficult to discuss all of these systematically because of limited data, but it would be easier to understand if you discussed them in such a concept. The analysis results seem diverse and complex, and multifactorial interpretation is required at each point. In other words, it is difficult to see the essence when considering it in an integrated manner.\n4) In addition, the involvement of a CDK4/6 inhibitor must also be considered. I would like this part (involvement of CDK4/6) to be as easy to understand as possible.\n5) The factors that make up the clinical effect, especially the duration of response, are multivariate. In addition to the factors discussed in this study, there are many others, such as tumour volume, heterogeneity, and the influence of previous treatment. As discussed, the immune system is also involved. It would be better to explain clearly which part was approached this time.\n6) The study of resistant clones is intriguing. However, it is limited to one cell line, and the data is challenging to interpret, especially when considering clinical data, and the current logical development seems unclear. A more complete study could be performed if experiments were conducted using multiple cell lines and under various conditions, including in vivo. Still, it would not be accessible in terms of time and quantity. If your research focuses on clinical analysis, I would like you to consider how to structure the section on resistant cell lines. It would be meaningful to analyse artificially resistant cells, cloned by exposing them to high concentrations of drugs for half a year, and explore how their behaviour, characteristics, and changes due to treatment compare with clinical results, and conversely, how the dynamics within the cell line correspond to clinical results. This may be a supplemental analysis, but it will help the reader understand. Finding a treatment to overcome resistance from this research might be possible, but on the other hand, someone may feel far.\nMinor:\n'Surprisingly, giredestrant did not significantly alter gene expression in FP cases. By contrast, giredestrant repressed a program of 135 genes in LTB tumors.' This result may not be surprising based on the previous data.", "reviewer_2": "The authors are addressing very important and timely question in a well defined cohort of patients included in a clinical trial. The data is biologically sound and novel, and the methodology used to address the questions appropriate. The statistics used is appropriate.\nThe flow of the work Is sound and conclusions robust and clinically useful. The paper addresses the most urgent question on how to potentially segregate patients in those who benefit from endocrine treatment (SERDs) in later lines and patients who don't.\nThe only potential lack of the study is that the model used encompassed only one cell line, however results were conclusive and the main focus on the clinical analyses and it even is not really needed to improve the manuscript with additional analyses. Maybe add a little emphasis on that in the discussion.", "reviewer_3": "In this paper, Liang et al investigated heterogenous response to next-generation SERD giredestrant +/- palbociclib (CDK4/6i) in a Phase 1a/b trial enrolling ER+HER2- metastatic breast cancer in the second or third line of therapy, identifying gene expression resistance pathways that emerged in fast progressors compared to long-term responders. Aspects of their analyses confirm expected results – comparable to those seen for example in the FELINE trial (Griffiths Nature Cancer 2021) – that fast progressors (intrinsically resistant tumors) have lower estrogen receptor pathway activity at baseline, less of a reduction of proliferation and ESR1 signaling with SERD treatment, and upregulation of orthogonal pathways including MAPK/ERK. Most results are derived from 29 patients with paired pre- and on-treatment tumor biopsies; they also draw from a larger cohort of 172 patients with some type of tumor or blood data. Overall the work is interesting, especially the suggestion that EGFR or MEK or ERK inhibition could overcome resistance in a subset of patients.\nMajor comments:\n1. It’s not readily apparent what the value is of the larger cohort of 172 with some type of biopsy. I do think more clarity is needed about which ones had tumor biopsy, which ones had liquid biopsy, and what timing. But in general, they do not appear to contribute substantially to a coherent story. For example – what does an early section of the results focused on concordance between tumor RNA-seq and ddPCR and F-ACT add to the main takeaways of this paper?\n2. Grouping tumors treated with giredestrant alone or giredestrant + palbociclib into one analytic cohort introduces bias and would be helpful if this were handled more explicitly. For example, given that the LTB were more likely to be treated with palbociclib, and that you grouped tumors exposed to single-agent giredestrant and palbociclib together, how do you ensure you aren’t identifying pathways associated with palbociclib treatment? While numbers are small, presumably some differences are seen in gene expression for tumors treated with giredestrant alone vs in combination with palbociclib – i.e. presumably palbociclib induces greater proliferation change (e.g. PALLET)?\n3. Is it so surprising that immune signatures were enriched in the progressors? Other work has shown that these tend to correlate well with estrogen expression (i.e. more estrogen activity is correlated with less immune infiltration), so given that progressors have less estrogen activity, perhaps it makes sense they have more immune signature enrichment. Without supporting clinical or preclinical data, I would not suggest that this would necessarily correlate to greater immunotherapy responsiveness.\n4. As above, I do find the cell line work suggestive and interesting, but would urge the authors to expand it a bit more. Ideally in vivo studies, or at least a couple more replicates.\nMinor comments:\n1. Intro: Need a citation for “Unfortunately, poor bioavailability limits the efficacy of fulvestrant in both ESR1-mutant tumors and the broader metastatic ER+ population.”\n2. Intro: Misspelled elacestrant (in intro and discussion)\n3. Intro: You mention that 30-50% were rapid progressors on trials like acelERA, EMERALD4, etc. What percentage of patients had sustained benefit up to 30+ months in these trials?\n4. Intro: would include information on trial eligibility and design early in results or in intro.\n5. Results: why does it say 172 for the larger cohort in the results, but 167 in the materials and methods?\n6. Results: I’m not sure how useful the comments on PAM50 subtype switch (e.g. Fig 1d) are. Proliferation is suppressed in the presence of drug, something that is sorted out better later, but this does not mean subtype was changed.\n7. Results: Is there any statistically significant difference of PAM50 subtype in FP vs MP vs LTB?\n8. Results: for the 135 genes suppressed on giredestrant therapy, how were these identified exactly? Were they differentially responding genes by some criterion?\n9. Results: Could you comment more (in the Discussion e.g.) on ESR1 mutation being enriched in the fast-progressing cases, especially given existing FDA approval for oral SERD is for ESR1 mutated tumors only?"}, "author_response": "To address concerns over small sample size, we profiled additional baseline biopsies (n = 27) collected on this study, which has nearly doubled our sample size from 28 to 55 biopsies. This empowered additional analyses, such as a comparison of single-agent vs. palbociclib combination-treated cases (see point #4 below), and enabled the use of other approaches to interrogate differences between responders vs. non-responders – e.g. generalized linear modeling (new Fig. 2c) and transcription factor enrichment analysis (new Fig. 2f). Statistical significance of prior analyses was also improved by these additional samples.\nWe have now added in-line discussions of relevant literature/data in the Results section when describing our study findings.\nIn both ER+ tumor biopsies and in vitro models, giredestrant resistance was associated with loss of ERα dependence and luminal identity, and up-regulated orthogonal pathways including MAPK and TEAD. The observed features are likely to promote inter-class resistance to a wide range of endocrine therapies since they bypass the need for ERα signaling altogether (e.g. ‘complete resistance to ET’ as the Reviewer describes). This is by contrast to ESR1 mutations, which primarily confer resistance to aromatase inhibitors by enabling ligand-independent ERα activity. To further explore this point, we examined cell lines which we engineered to acquire resistance to giredestrant and another investigation anti-ERα drug, GDC-0810. The two drugs represent distinct mechanisms of pharmacological ERα inhibition: whereas giredestrant is a full SERD, GDC-0810 is a SERD-SERM hybrid. The same features (loss of ERα dependence and luminal identity) emerge in vitro when cells acquire resistance to either drug. Both giredestrant- and GDC-0810-resistant MCF7 and T47D cells develop cross-resistance to standard-of-care ERα therapeutic ligands: the SERD fulvestrant and the SERM tamoxifen (new Figure 3, new Extended Data Fig. 7). We have added a discussion of these points in the first paragraph of the Discussion section.\nWe agree that multifactorial interpretation should be considered – we address this as described in point #5 below.\nThis is an important point, since patients who received the giredestrant-palbociclib combination were more likely to be responders (new Extended Data Fig. 3a-c). We have now restructured our analyses to explicitly address this point. We now begin with a comparison of responders vs. non-responders in the single-agent giredestrant treatment arm (new Figure 1). Next, since the majority of patients in the palbociclib combination arm were responders (PFS > 2 months), we compared single-agent responders to palbo-combination responders (new Extended Data Fig. 4f-g, Figure 2).\nWhile single-agent responders had highly ERα-active and luminal tumors, patients who exhibited response in the palbociclib combination arm exhibited significantly more variation in these features, comprising a mix of ERα-high and ERα-low tumors (p = 0.001 and 0.01 by two-sided F-test for variance). We hypothesize that while ERα dependency is linked to response in the context of giredestrant monotherapy, the combination with a CDK4/6i provides orthogonal suppression of tumor growth independent of the ERα signaling axis. We now highlight these points in paragraph 3 of the Discussion section.\nAs the Reviewer highlights, the determinants of treatment response may be complex and multifactorial. To address this point, we performed univariate Cox proportional hazards modeling on key clinical features to assess the influence of each on progression-free survival in n = 181 enrolled patients (new Extended Data Fig. 3a). Features related to patient health (e.g. age, BMI, ECOG), disease characteristics (ESR1 mutational status, histological subtype, tumor stage/grade, TNM staging) and treatment history (chemotherapy, fulvestrant, CDK4/6i) were evaluated. Critically, tumor heterogeneity and the immune system were not evaluable since our clinical trial was not designed to capture these features.\nThe only features associated with PFS were related to CDK4/6 inhibitors (Extended Data Fig. 3a-b). Patients who received prior CDK4/6i had significantly worse PFS, and patients enrolled in the palbociclib combination arm had improved PFS. These two effects are difficult to differentiate, since most of the patients in the palbociclib combination arm had no prior CDK4/6i exposure (Extended Data Fig. 3c). These analyses, and explanation of which specific features were evaluated, are now described in paragraph 1 of the second Results section. In addition, we have now re-structured our analyses of patient data to account for the profound influence of palbociclib combination on responses (see point #4 above).\nWe thank the Reviewer for this suggestion. We have now greatly expanded the pre-clinical experiments on this study, including the Reviewer’s suggestion of exposing cells to high concentrations of drugs and profiling their changing characteristics over the course of treatment. Deep profiling experiments were performed on two different workhorse models of ER+ breast cancer: MCF7 and T47D (new Figs. 3-4, Extended Data Fig. 6, 8). We additionally interrogated giredestrant resistance across a panel of 14 different ER+/HER2- breast cancer cell lines (new Extended Data Fig. 9). The new data are limited to in vitro experiments, since in vivo studies were not feasible in terms of time or quantity as the Reviewer suggests.\nThe new data are described in the latter 3 sections of Results; below are some highlights:\n• Over 4 months of giredestrant treatment, MCF7 and T47D cells acquired resistance to giredestrant (GiredR) via loss of ERα and luminal lineage identity, as seen in giredestrant non-responsive patients (NR). GiredR cells also acquired activity in NR-enriched pathways (e.g. MAPK, TEAD) over treatment.\n• Across 14 ER+/HER2- breast cancer cell lines, resistance to giredestrant was significantly correlated with low ERα activity and elevated MAPK/TEAD signaling.\n• Assessing the chromatin landscape over treatment, we found that ERα-associated chromatin sites became suppressed, followed by the induction of ERα-independent, MAPK/TEAD sites. FOXA1 and FOXM1 (which normally regulate ERα) were significantly associated with the MAPK/TEAD sites induced in GiredR.\n• GiredR cells exhibited increased sensitivity to inhibitors targeting the NR-enriched pathways, MAPK and TEAD, although the degree of sensitivity varied between models. Inhibition of FOXM1, which was implicated in both NR biopsies and GiredR cells, drove a complete loss of cell viability in both parental and GiredR cells.\nWe have removed the term ‘surprisingly’ in the relevant text.\nWe thank the Reviewer for this assessment of our manuscript.\nThe limited exploration in cell line models was highlighted by multiple Reviewers. We have now greatly expanded our cell line experiments to model giredestrant resistance with a molecular deep-dive on the two most common ER+ breast cancer cell lines, MCF7 and T47D. We additionally explore the association between giredestrant resistance and clinically-identified transcriptional features in a panel of 14 ER+ breast cancer cell lines. In particular, we have identified the transcriptional and epigenetic events that drive endocrine resistance and identified additional therapeutic options in pre-clinical experiments on cells with acquired resistance to giredestrant. Please see our response to Reviewer 1, comment #6 above; the new data are described in Figures 3-4 and Extended Data Figs. 6-8.\nWe thank the Reviewer for this assessment of our manuscript, and strongly agree with the need to identify therapeutic strategies to overcome resistance to endocrine therapies. In this revised manuscript, we have elaborated on this point by further exploring the transcriptional and epigenetic events that drive endocrine resistance and identifying additional therapeutic options (e.g. TEAD inhibition) in pre-clinical experiments on cells with acquired resistance to giredestrant.\nGiven the recent approval of elacestrant for ESR1-mutant disease and multiple ongoing studies using ESR1 mutational status as an enrollment criteria (e.g. evERA [NCT05306340], pionERA [NCT06065748]), we initially felt that concordance data between multiple ESR1 assays would be of interest to the ER+ breast cancer community. PIK3CA mutation prevalence is likewise relevant for alpelisib and recently-approved inavolisib. However, we agree with the Reviewer’s suggestion that the relevant section of text was excessive and detracted from the main focus of our manuscript, which is to understand why some patients benefit from next-generation SERDs while others do not. While we still feel the community may derive value from these mutation/concordance data, we have now minimized our description of the data into two sentences of the main text and abridged the data panels into Extended Data Fig. 2. In addition, we have streamlined our text and methods by removing mentions of biopsy numbers, except in instances where this information is relevant for data interpretation.\nThis is an important point – in our cohort, prior exposure to CDK4/6 inhibitors was poorly prognostic, while patients who received the giredestrant-palbociclib combination more likely to respond (Extended Data Fig. 3a-c). These results parallel that of the recent EMBER-3 trial on imlunestrant, in which similar patients receiving the combination of imlunestrant and abemaciclib had stronger responses than imlunestrant monotherapy (HR = 0.57, p < 0.001).\nTo address this point, we have restructured our analyses to start with a comparison of single-agent responders vs. non-responders (new Figure 1). Since the majority of patients in the palbociclib combination arm were responders (PFS > 2 months), we next compared single-agent responders to palbo-combination responders (new Extended Data Fig. 4f-g, new Figure 2). By separating the single-agent responders from palbo-combination responders, we increased our confidence that the identified pathways/features were associated with giredestrant response, rather than a palbociclib effect. While single-agent responders had highly ERα-active and luminal tumors, patients who exhibited response in the palbociclib combination arm exhibited significantly more heterogeneity in these features, comprising a mix of ERα-high and ERα-low tumors (p = 0.001 and 0.01 by two-sided F-test for variance). We hypothesize that while ERα dependency is linked to response in the context of giredestrant monotherapy, the combination with a CDK4/6i provides orthogonal suppression of tumor growth independent of the ERα signaling axis. We now highlight these points in paragraph 3 of the Discussion section.\nThis is a good point – we have removed any speculation in the text regarding the responsiveness to immunotherapy, since we lack clinical or pre-clinical evidence on this topic.\nWe thank the Reviewer for this suggestion. We have now greatly expanded our cell line experiments to model giredestrant resistance with a molecular deep-dive on the two most common ER+ breast cancer cell lines, MCF7 and T47D. We additionally explore the association between giredestrant resistance and clinically-identified transcriptional features in a panel of 14 ER+ breast cancer cell lines. In particular, we have identified the transcriptional and epigenetic events that drive endocrine resistance and identified additional therapeutic options in pre-clinical experiments on cells with acquired resistance to giredestrant. Please see our response to Reviewer 1, comment #6 above; the new data are described in Figures 3-4 and Extended Data Figs. 6-8. The new data are limited to in vitro experiments, since in vivo studies were not feasible in terms of time or quantity.\nWe have now included the appropriate citations.\nThis has been corrected.\nAs our earliest study, the current phase-1 giredestrant trial has had the longest follow-up time, and some patients were tracked beyond the 30 month timepoint. However, publicly-available data on the remaining studies do not extend to 30 months of follow-up. Below, I include the percentage of ITT patients in respective investigational arms who remained on-study for the maximum duration of follow-up. In the acelERA BC study, ~15% of patients in the giredestrant arm remained on-study for 12 months. In the EMERALD study, ~15% of patients in the elacestrant arm remained on-study for 25 months. In the SERENA-2 study, ~20% of patients in the 75 mg camizestrant arm remained on-study for 25 months. In the recent EMBER-3 study, ~10% of patients in the imlunestrant arm remained on-study for 28 months. Note that each study had different eligibility criteria, so direct comparison across studies cannot be made. We have updated the language in the Introduction section to “patients had sustained benefit up to 2+ years”.\nWe have now included this information in the first paragraph of the Results section.\nPer major comment #1 above, we appreciate the confusion caused by the various biopsy numbers. To address this, we have streamlined our text and methods by removing mentions of biopsy numbers, except in instances where this information is relevant for data interpretation.\nWe have removed the discussion of this point from the manuscript text, and moved the relevant figure panel to Extended Data Figure 1.\nTo address this question, we included a new figure panel to compare baseline PAM50 subtypes across response groups (new Extended Data Fig. 1f). There is no significant difference in PAM50 subtypes, and the relative proportion of LumA to LumB is similar between response groups.\nThe 135 genes were identified unbiasedly via differential gene expression analysis. We compared differential gene expression between the baseline and on-treatment (C2D8) biopsies from LTB patients. The analysis was performed using the limma-voom method, with statistical cutoffs as follows: log -fold change ≥ 1 (up at C2D8) or ≤ -1 (down at C2D8), FDR < 0.1. We have revised the main text to highlight our analytical approach.\nAlso note that in this revised manuscript, we have followed reviewer suggestions to streamline our analyses, and have grouped the former MP (medium progressers) and LTB (long-term benefitters) into a single group, called responders (Resp). We now include the same analysis on the Resp group, which identifies 144 genes which are suppressed by giredestrant (new Fig. 1b-c), computed using the same method as described above.\nIn our cohort, ESR1 mutations are not enriched in fast-progressing cases*. Rather, both non-responders and responders have ESR1 mutations in ~30-40% of cases (new Fig. 2b; non-significant by χ2-test). By Cox proportional hazards modeling, ESR1 mutational status is not significantly associated with differences in progression-free survival (new Extended Data Fig. 3). As the Reviewer notes, these observations initially appear paradoxical, since the FDA approval for oral SERD is specific to ESR1-mutated disease. However, upon closer examination of recent study outcomes (acelERA BC, EMERALD, SERENA-2, and EMBER-3), this discrepancy can be explained by two factors.\n(1) ESR1 mutations are poorly prognostic on standard-of-care therapy, but experience improved outcomes on next-generation SERDs. This led to the approval of elacestrant for ESR1-mutated disease.\n(2) When mutant and NMD disease are both treated with next-generation SERDs, the difference in prognosis becomes non-significant (as in Fig. 2b, Extended Data Fig. 3a-b). Using the acelERA BC as an example: the median PFS for ESR1-mutant patients receiving standard-of-care was 3.8 months, ESR1-mutant patients receiving giredestrant was 5.3 months, and all patients (ITT; includes both ESR1-mutant and NMD) was 5.4 and 5.6 in standard-of-care and giredestrant arms. We have now dedicated the second paragraph of the Discussion section to explaining this point. (*During manuscript revisions, we identified a mislabeling in the previous Fig. 4a, which may have added to the confusion; this error has now been corrected.)"}, {"round": 2, "reviewer_comments": {"reviewer_4": "I have no further comments for the authors. The prior comments raised have all been adequately addressed, and the manuscript should be shared with the broader community.", "reviewer_6": "This study addresses an important clinical issue: the variable response to next-generation SERDs in advanced ER⁺/HER2⁻ breast cancer. Using multi-omics data from a giredestrant clinical trial and in vitro resistance models, the authors conclude that loss of luminal lineage identity, rather than ESR1 mutations, underlies resistance to ERα-targeted therapy. The study is strong, methodologically rigorous, and offers meaningful mechanistic and clinical insights. However, some key concerns are needed to be addressed.\nMajor Comments:\n1. The authors provided strong evidence for the role of FOXA1 in driving a resistant phenotype through chromatin remodeling. However, the mechanism by which FOXA1 expression and activity are upregulated in the resistant cells remains unclear. Is this a stochastic event, or is it driven by a specific upstream signaling pathway? Furthermore, the authors show that ~70% of FOXA1 binding sites in resistant cells are not co-occupied by ERα. What are the transcriptional partners of FOXA1 at these sites, and what are the key target genes that drive the resistant phenotype? Have the authors considered performing ChIP-seq for FOXA1 in their resistant cell lines to identify its direct targets?\n2. The study focuses on a specific mechanism of resistance involving the loss of luminal lineage. However, it is likely that other mechanisms of resistance also exist. The authors themselves show that a subset of non-responders still retain some ERα activity. How do the authors envision the interplay between the mechanism they have identified and other known resistance mechanisms, such as the acquisition of ESR1 mutations or the activation of other receptor tyrosine kinases? A more nuanced discussion of the heterogeneity of resistance would strengthen the manuscript.\n3. The manuscript demonstrates upregulation of multiple bypass pathways (EGFR/MAPK, Hippo/TEAD, Wnt, IL2/STAT5) in non-responder tumors. However, it remains unclear whether these pathway activations are drivers of resistance or merely consequences of losing ERα-dependent growth control. Have the authors performed functional validation experiments (e.g., genetic or pharmacological inhibition of these pathways) to determine whether blocking these pathways can restore giredestrant sensitivity?\n4. The observation that different resistant cell lines (MCF7-GiredR vs. T47D-GiredR) show differential activation of bypass pathways (MAPK in MCF7 but not T47D) suggests context-dependent resistance mechanisms. What determines which bypass pathway becomes activated in a given cellular context? Are there pre-existing differences in signaling pathway wiring between MCF7 and T47D cells that predispose them to different resistance mechanisms?\n5. The authors identify TEAD4 and TCF4 as transcription factors associated with non-responder genes, implicating Hippo/TEAD and Wnt signaling. However, canonical activation of these pathways typically involves nuclear translocation of YAP/TAZ (for Hippo) or β-catenin (for Wnt). Have the authors examined the subcellular localization and activation status of these key signaling mediators in resistant cells? Are there genetic alterations in pathway components that would explain constitutive activation?\n6. The authors also generated resistance models using GDC-0810 (a SERD-SERM hybrid) and observed similar features. Have they tested other next-generation SERDs (e.g., elacestrant, camizestrant) to determine whether this resistance mechanism is common to all SERDs or specific to certain chemical scaffolds?\nMinor Comments:\n1. In Figure 2, the authors show that several transcription factors are associated with non-responder genes. It would be helpful to show the expression levels of these TFs in the responder versus non-responder patient samples.\n2. The authors mention that the MCF7-GiredR cells have deletions on chromosome X. Are any of the deleted genes known to be involved in epigenetic regulation or ER signaling? A more detailed discussion of the potential functional consequences of these deletions would be welcome.\n3. The sample sizes for some comparisons are relatively small (e.g., n=10 NR vs. n=17 Resp pairs for the on-treatment analysis). Have the authors performed power calculations to determine whether they have sufficient statistical power to detect meaningful differences?\n4. Ensure consistent use of drug-class terminology (e.g., “next-generation ERα-antagonists,” “SERD-like,” “PROTAC-like”), and consistently name or classify the compound used throughout the manuscript.", "reviewer_7": "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": "We thank the Reviewer for providing thoughtful feedback, which has greatly improved our manuscript.\nWe thank the Review for providing thoughtful feedback, which has greatly approved the manuscript. As described below, we have added new data (Figure 4f-g, new Extended Data Figure 9, and Extended Data Figure 10c-d) to address the Reviewer’s comments. We have also edited the relevant manuscript text to clarify key points or describe the new data. For the Reviewer’s convenience: in the revised manuscript, text which has been added/edited since the previous submission is colored in red while unaltered text is in black.\nMajor critiques:\n1) While the primary data from the in vitro models has been deposited in GEO, the RNA-seq and liquid biopsy data from the trial should also be made available.\nWe wholeheartedly agree with the importance of data sharing in research and make our data publicly available whenever possible. Unfortunately for this clinical trial, we do not have the patients’ consent to publicly share their sequencing results with the scientific community. Prior to participating in our clinical trial, patients signed an informed consent form, which stipulated the terms under which their data could be used. Two key terms of data usage are: (1) our organization is only allowed to use the patients' data for a fixed amount of time (duration varies by country laws), after which we are legally/ethically required to delete the data and (2) patients have the additional right to withdraw their consent for us to use their data at any time (some patients still remain on this study). This means that we can publish any analyses we performed prior to the time limit and/or consent withdrawal, but we cannot make individual patients' sequencing data publicly available in perpetuity.\nAs an alternative, we will provide all sequencing-derived values used in this manuscript (e.g. per patient ER activity scores, luminal TF scores, etc.) as source data, which can still be of great benefit to the community.\n2) The authors argue that in the giredestrant resistant models that the inability to identify a clear genetic mechanism of resistance suggests that resistance is primarily epigenetically driven. The data presented do not support this conclusion. The stable resistance phenotype of GirdR models may indicate genetic selection and there may be different genetic mechanisms in MCF7 or T47D cells. In addition, there may be multiple different genetic mechanisms in distinct clones present in the GirdR cultures that may mask distinct genetic resistance mechanisms. Single cell cloning or sequencing would be needed to address this possibility. In Figure 3e, using whole exome sequencing (WES), over 50 mutations are enriched in GirdR cells, including known cancer-associated variants, but follow-up experiments are not performed. Additionally, copy number variation (CNV) analysis was performed on GirdR cells, which identified a loss on chromosome X including 19 cancer associated genes (Line 335), but this data is not presented in the figure and there is no validation. Deletions found in the CNV analysis further supports genetic selection of a SERD resistant clone. The authors state “Since few genetic mutations were acquired by GiredR cells, we considered whether epigenetic alterations may drive ERα independence and loss of luminal identity” (Line 362), and in Figure 5 they propose an epigenetic model of endocrine resistance. The authors should point out more clearly that genetic mechanisms of resistance remain a likely explanation despite their inability to identify them.\nWe agree with the Reviewer’s assessment, and have made multiple changes to address this point. We underscore the fact that our data does not preclude genetic mechanisms of resistance as follows:\n• We now conclude the whole exome sequencing results paragraph by explicitly stating that “genetic drivers for giredestrant resistance remain a possibility.” (line 343).\n• At the start of the following section, we have replaced the first sentence (line 362 in the previous version, now line 364) with “We considered whether epigenetic alterations may promote ERα independence and loss of luminal identity upon prolonged ERα inhibition,” to avoid the implication that genetic vs. epigenetic mechanisms of resistance are mutually exclusive.\n• For the cartoon model in Figure 5, we have revised the title into a descriptive statement: “The shifting epigenetic landscape in ERα-independent, giredestrant-resistant disease”.\n3) This manuscript relies heavily on computational imputation without validation. For example, in Figure 4d-f, ATACseq identified chromatin opening at FOXM1 motifs, which the authors argue is a novel driver of endocrine resistance (Line 34), however the ATACseq results do not necessarily demonstrate a functional role for FOXM1. For example, the FOXM1 motif highly resembles the FOXA1 motif, a known driver of endocrine resistance. A direct role for FOXM1 should be validated using FOXM1 ChIPseq and testing whether FOXM1 is necessary for resistance in GirdR cells and sufficient to induce resistance in parental cells.\nTo directly test the roles of FOXA1 and FOXM1, we have performed ChIP-seq against both factors in parental vs. GiredR cells for MCF7 and T47D models. The new ChIP-seq data confirm differential binding of both FOXA1 and FOXM1 in GiredR cells, and corroborate the prior ATAC-seq findings. FOXM1 is enriched at 823 and 3,222 sites in GiredR cells vs. parental, for the respective models. The data are presented in the Figure 4f-g and Extended Data Figure 9 of the revised manuscript, and described on lines 414 – 432. Raw ChIP-seq data has been uploaded to the GEO under GSE305432.\nWe also explored genetic perturbations of FOXM1. Overexpression of FOXM1 in MCF7 cells was previously linked to endocrine resistance, and drove increased aggressiveness and metastatic potential (Yang et al. 2013, Bergamaschi, et al. 2014). We therefore focused on the effect of FOXM1 siRNA in parental and GiredR cells (new Extended Data Fig. 10c-d). FOXM1 siRNA in parental cells suppressed cell growth by Incucyte measurement, consistent with a prior report (Yang et al. 2013). However, FOXM1 siRNA did not alter the growth of either GiredR model. Together, the data suggests that although FOXM1 has an expanded range of chromatin binding in the GiredR state, its knockdown does not affect growth rate in GiredR cells.\nGiven these data, we have refrained from describing FOXM1 as a “driver” of resistance and removed the statement in the Discussion section that speculated on the “potential for FOXM1 as a therapeutic target”. We have also de-emphasized the FOXM1 results by moving them to Extended Data; Figure 4 now focuses on FOXA1, which the Reviewer highlights as a known driver of endocrine resistance. Nonetheless, our single siRNA experiment does not preclude other functional roles for FOXM1, such as establishment of resistance over the course of treatment. Further experiments on FOXM1 may remain worthwhile – for example, it would be interesting to test whether FOXM1 knockdown affects the rate at which parental cells acquire giredestrant resistance when exposed to long-term treatment.\n4) The use of the FOXM1 inhibitor FDI-6 is not convincing. What is the evidence that its effects are on-target? Can the effects be reproduced genetically?\nWe have removed the FDI-6 data from the manuscript along with associated text, and instead explored genetic perturbation of FOXM1. Those results are described in our response to point #3 above.\n5) The model presented in Figure 5 is not supported by the data presented.\nWe have now modified the model to capture key conclusions supported by our data. We feel this revised model is supported by either (1) data which are newly-presented in this manuscript or (2) are established concepts in the field. Specific changes include:\n• FOXA1 and FOXM1 are illustrated as binding to the ER-independent, GiredR-induced sites, as supported by our new ChIP-seq data.\n• We have removed any additional transcription factors from the GiredR-induced sites (e.g. TEAD), since their previous inclusion was only based on ATAC-seq motif enrichment analyses.\n• We now clearly distinguish conclusions from the patient vs. cell line data by dividing the model’s layout and including labels specifying the transition from parental to GiredR states in vitro.\n• In endocrine-sensitive cells, ER is depicted as associating with FOXA1 and FOXM1 to regulate ER-associated chromatin sites. While we do not perform ER ChIP-seq in our study, we feel this portion of the model is firmly supported by multiple prior publications (Hurtado et al. 2011, Sanders et al. 2013, and others).\nOther questions and minor concerns:\n1) Are any ESR1 gene amplifications, deletions, or gene fusions identified in patients?\nWe did not detect any ESR1 copy number alterations (amplifications and deletions) among the n = 123 patient samples tested on our study. Gene fusions involving ESR1 were also not detected.\n2) The methods section indicates that WES was performed using a mouse genome kit (Line 1045).\nApologies for this oversight - the WES methods language was copied from a prior manuscript in which WES was performed on mouse tissues. We have now corrected the corresponding methods section to reflect the SureSelect Human V7 kit, which was used for these experiments.\n3) Please define NMD (Line 128) and specify the genes for the Luminal TF (Figure 1F) and the MAPK signature (Ext Data Fig 9a) in this manuscript, and update Ref #26.\nWe have now defined NMD (no mutation detected) on line 128, and included the genes comprising the requested signatures in the Methods, under the “Gene signature scores” sub-section of “Bioinformatics data analysis”. Reference 26 has also been updated.\nWe thank the Reviewer for providing thoughtful feedback, which has greatly improved our manuscript.\nChIP-seq against FOXA1 was performed in both MCF7 and T47D parental and GiredR cell lines (Figure 4f-g, Supplementary Fig. 9b-d). Together, the data support altered FOXA1 binding in parental vs. GiredR cell lines. In GiredR cells, FOXA1 is bound to chromatin sites containing TEAD1/4 motifs, AR motifs, and NFI-family motifs. Regarding the “upregulation of FOXA1 activity” following long-term giredestrant treatment, we posit that the absence of functional ERα frees FOXA1 to bind/regulate ER-independent chromatin regions.\nThis is an excellent point, and we now address this in the Discussion section of our manuscript. Multiple mechanisms are likely required to drive resistance to giredestrant. In particular, the primary effect of luminal lineage loss is to abrogate ER dependency; this is insufficient to enable sustained tumor proliferation. Instead, other pathways such as MAPK/RTK or Hippo/TEAD signaling are needed to sustain tumor cells.\nIn Figure 4i, we tested whether GiredR cells acquired sensitivity to MEK (MAPK pathway) and TEAD inhibition. GiredR cells were more sensitive to growth inhibition via MEKi or TEADi than parental cells, although the effect was more pronounced in MCF7-GiredR than in T47D-GiredR. Thus far, we have not tested inhibitors of Wnt or STAT signaling; per feedback from the Nature Communications editors, additional experiments are beyond the scope of this manuscript.\nThis is a very interesting question - we have added a discussion of this point in the Discussion section of our manuscript. It is highly plausible that baseline differences in genetic background and epigenetic state across cell lines would predispose each cell line towards acquiring different bypass mechanisms.\nBased on the mutational profiling performed on patients, we did not observe genetic alterations in TEAD or Wnt pathway components which could explain the increased pathway activity in NR patients (Supplementary Fig. 2c). Thus far, we have not examined the localization or activity status of TEAD or Wnt in GiredR cells; per feedback from the Nature Communications editors, additional experiments are beyond the scope of this manuscript.\nWe have not generated resistant cell lines from other next-generation SERDs. However, based on the known pharmacology of other SERDs like elacestrant or camizestrant, we predict that this resistance mechanism would apply to both oral SERDs and SERD-SERM hybrids, since loss of ER activity/dependence would fundamentally render breast cancer cells insensitive to any ER-targeted therapy. Per feedback from the Nature Communications editors, additional experiments are beyond the scope of this manuscript.\nPer feedback from the Nature Communications editors, additional data are beyond the scope of this manuscript revision.\nSeveral deleted chrX genes (including ATP6AP1) have been linked ER activity. We will expand our discussion in the corresponding results section of the manuscript text.\nThe paired analyses (Supplementary Fig. 4a) were limited due to difficulties in collecting matching baseline and on-treatment specimens in our clinical trial. In a power calculation (assuming 80% power and α = 0.05), our minimum detectable effect size was d = 1.15.\nWe have revised our manuscript to ensure consistent use of drug-class terminology.\nWe thank the Reviewer for contributing to the review process."}]

Decision letter